I have some sympathy for these kids. If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well; many of them can no longer brainstorm, code, think deeply, or write without an LLM present doing 90% of the work. Many of them can no longer sit quietly for even 30 minutes just thinking on their own, which is a required skill for producing original thought.
For adults the cognitive decline won't be as measurable since there's no exams, and overall output volume will still be fine due to LLM help. But I do believe it's already happening absolutely everywhere around us. Honestly, I wanted to be in denial about it before but it's too obvious to ignore now.
I’m not noticing the decline in my own abilities any more than I had before using them. I finished undergrad 20 years ago and my once sharp math skills had been severely diminished within only 5-10 years. Just simple arithmetic and percentages that I could rapidly do in my head became dependent on calculators/spreadsheets. For all other trivia type knowledge, my brain has offloaded it to the internet RAM in my pocket. It’s a familiar feeling of when some question comes up and I think “oh, I used to know that, let me look it up”. Maybe I just already hit my personal floor of stupidity before LLMs.
However, I personally feel a huge mental burden of the state of communication. The contemporary version of it where I have a million threads and conversations im juggling at any given time. Emails, voicemail, chat, online, texts, personal, business, home, children, other family, friends, then there’s the variants like Messages, Messenger, WhatsApp, etc. And as overwhelming as it is for me, I’m super under connected than everyone else I know. I quit following most news and all sports, as I just don’t have the bandwidth for it.
My brain was molded preinternet and I feel like it’s reaching its max on the analog to digital conversion. Or at least it’s just a really lossy process.
Yeah, I'm 45 and I'm like you - no social media, relatively under connected, and still feel swamped constantly by emails and calls and especially texts. They eat up half my productive time every day, and most of them are things I'm looped in on that I don't even need to respond to.
Okay so let's say that's the new cognitive burden. The new escape hatch is "AI". Now you don't need to read your mail or write responses! Let an LLM handle that for you! And now your friends and coworkers will send you AI generated mail anyway, so if you're actually taking the time to read and respond to it yourself you're a chump, right?
Noise machines. Humans are noise machines. Ever try to sleep till noon and notice that everyone else seems like they can't feel alive unless they wake up and make the maximum amount of noise and racket possible? What could be better for a gibbering species of ground dwelling apes than a miraculous machine that gibbers for them, to point back and forth at each other?
I think some people are okay with communication that’s less involved. Like meme-y BSing where everyone involved knows everyone else is putting like 12% of their thinking power into sending a response.
I don’t really enjoy that, so I find having that many threads stressful and annoying.
I just take a hard line and will unilaterally downgrade communications (while politely letting the other party know). I have all my family group chats muted because my mom uses “Send” the way you’d use Enter on a desktop. End of a sentence? Send text. Next bullet point in a list? Send text.
I muted the chats and told her that I want my ringer on in case there’s an emergency, but I got 30 something notifications in 5 minutes during an interview and it’s unfair to the candidate or other people in the meeting. Internally I rationalize it as revoking someone’s ability to make noises on my phone at whim. They can still text me, they just can’t interrupt me anymore.
It helps a lot, even if only temporary. I’ve muted people for a few hours or a couple days before when I’m already stressed and they’re really chatty.
I'm noticing some decline of skills I don't practice regularly and LLM is just one of reasons why one stops practicing. Switching to another area of work gives a comparable decline. If you want sharp skills you have to use them.
Most/all of my university-level math knowledge is gone, atrophied from never having needed to use any of it professionally. I don't even really recall needing it for any of my CS coursework, honestly. It was just required for the degree.
I don't think it's just you or your age, per your pre-internet comment. People that grew up in this just don't understand why they're overwhelmed. And I don't think they're even aware of what their missing out on in terms of focus or mental acuity.
I too was and wanted to only blame communication overload. Especially with work the hardest thing in ai times seems to be the overload of stuff/shit to read that is too easy to write.
The reality is I agree with the op and I see the loss of reasoning power in myself. I've been using native Emacs on android for a bit and finally have gotten serious about config for it. I got lazy and had Claude do some of it. Which was great untill things don't work because there's not going to be my crazy ask in the data. It was painful for me to sit down and think through my configuration and the problem but I did it.
I am absolutely torn on the technology still two years after adopting it.
It’s a really lossy process. Mostly due to most humans and all models treating sign meetings as determined at the moment of softmax crystallization. Signs (words included) are no more determined than the speed of light is. It’s all reflexive and we should stop lying to ourselves it can be determined.
I used Google Translate to not learn French in collage. Fortunately for me it was bad enough I had to carefully review all its outputs, but that still didn't help and I managed to pass two semesters without ever developing even basic language skills.
Something radical needs to be done. When I was in high school there were still a lot of "no calculator" restrictions in my math classes that I chaffed at because I hated doing longform arithmetic and felt like it got in the way of learning. So I can certainly understand how students would chafe at some kind of paper-only education system but I also don't see how you can learn anything when you have a high-quality homework machine just sitting there.
I wish that would have worked for me - we had oral tests. 2 years of French in high school and one semester at college - what an absolute waste of time. How much French do I know now? Basically none. The same goes for everyone in my life that did Spanish instead in high school.
Part of what we could do during this upset is re-prioritize.
All that's needed is a tight feedback loop between learning and applying those skills ... the thing that Google Translate helped you evade. AI can be a tool for evading or optimizing that loop, like a knife can cut your sandwich or your throat. Your choice.
> If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
I agree - I would have been toast. I wonder if the teachers/colleges need to change the way they teach and assess. Let the students use the AI tools they like (perhaps guide them how they can use them professionally), but test regularly and early on the skills/knowledge they're meant to be gaining offline and in person. Oh and don't give Fs for cheating - suspend them.
I read a few years ago about a teacher (I think highschool) who put his lectures on YouTube for students to view in their own time and then used the in class hours for interaction, questions, tests.
Absolutely university has to change. But it's not a simple change. I say this as a professor for Physics:
My colleagues say "We must fully embrace AI as a tool". I agree. But how do you teach it? It's a moving target, and you can't even give homework like: "Research <this topic> with an LLM of your choice, and submit the transcript" because they can do that, or they can just copy the task into an LLM and have the LLM do it. It becomes meta quite quickly.
And independent what and how we teach, we have to change how we assess a students learning result:
The first thing we have to change is that homework needs to be completely ungraded. Reviewed and corrected, yes, but not part of the grade. That's the only way to make sure that people who don't want to cheat have to cheat anyway to compete with those that do.
Second, all exams have to be in person. Online, cheating is so trivial it's not even funny (many students are so stupid about it that we have a pretty clear idea what's going on). In person, we have maybe 2-3 years until we have to make sure its proctored and people's glasses are checked. I think in less than 10 years, local mobile AI will be good enough so even a Faraday cage will not help.
Maybe we have to go to oral tests only.
Of course, none of this scales. Some of our intro courses have a thousand students.
>(perhaps guide them how they can use them professionally)
If that's anything like how they guided me to use programming languages professionally...
In my workplace I find systems and policies move too slowly to keep up with how rapidly the LLM world is changing. Colleges are even more glacial. They've barely adapted to video conferencing.
> I read a few years ago about a teacher (I think highschool) who put his lectures on YouTube for students to view in their own time and then used the in class hours for interaction, questions, tests.
That seems like a smart approach. It reverses the traditional model of "lecture in class, homework outside of class".
> Let the students use the AI tools they like […], but test regularly and early on the skills/knowledge they're meant to be gaining offline and in person.
I very much doubt there is any agreement on what those skills are.
Creating the idea of “what to learn in the new world” is itself IMO an important academic creation, but there’s no reward for doing it and no way to know if you’re on the right track (you just have to wait and see).
Employers are also just adapting.
Wait until companies are paying unsubsidized “list price” for LLM usage. Then we can have a better idea of the worth of the automation and what skills should stay with humans.
I'm dumb as a rock and I don't have a PhD, but since ~1 year ago I started forcing myself to do small bits of coding and math manually.
I'm not noticing a "cognitive decline" per se, but I do see I'm a lot "lazier", even stuff that used to be routine when I started coding now feel heavy.
> but I do see I'm a lot "lazier", even stuff that used to be routine when I started coding now feel heavy.
Not getting that quick dopamine hit the LLMs give you..
Some say you can re-train your system to get back the dopamine hits you used to get from other things, like the enjoyment of the "old fashioned" manual coding and math. Getting there is hard work. And YMMV.
>even stuff that used to be routine when I started coding now feel heavy.
The same weight feeling heavier is a sign that your muscles are weaker :)
There's many areas in life were we look back a few decades and think "people use to do it that awkwardly?" And yet results were better. I think the process of removing friction have just served to destroy our ability to concentrate and tolerate difficulty.
I do a similar version of this, where if I notice a mistake in generated code, I fix it manually (or at least attempt to) instead of telling Claude to fix it.
> For adults the cognitive decline won't be as measurable since there's no exams, and overall output volume will still be fine due to LLM help
The leading indicator for me is the amount of emails and, god forbid, more personal messages (like birthday wishes!) I see that are obviously AI generated. It just keeps on rising. If you’re not able to dash off a quick message without the help of AI I have to assume you’re using it heavily elsewhere too.
I have sympathy for the university students too, we’re all bombarded with rhetoric about AI being the future. And I remember being incredibly nervous emailing my lecturer (am I phrasing this right? Is it respectful enough?) that I can imagine leaning on AI myself had it been available back in the day. But I’m glad it wasn’t, it’s an important skill to work out this stuff. They’re going to land an in person interview when they graduate and stumble around unable to effectively answer the questions they’re asked in real time.
I broadly agree with the premise. As a PhD student in Computer Science, I feel there are some significant upsides to my work routine. LLM access has made many new domains more "accessible" to me which I otherwise would be too hesitant in investing my time in.
For example, my area of research is computer systems which involves operating systems, distributed systems and more recently systems for AI. Within these, there is a wide breadth of topics/techniques one can employ and up until now, I have not gone deep into theoretical aspects of things like scheduling etc. But with access to LLMs, I feel like I can at least brainstorm from a high-level about these sub-areas that I am not well-versed in and the responses give me some relevant pieces to start exploring on my own, depending on what interests me more or the amount of time I want to spend on that sub-branch of a larger tree of ideas.
However, the one thing I do have skepticism is the lack of awareness of blind-spots when dabbling into areas that I am not an expert in, and taking the LLM's lead in applying such techniques to some systems problems that I am working on. I often feel that I am not aware of what alternatives exist that the LLM has not explored for me, or if the directions it has proposed really do apply or have corner cases/assumptions that break in what I am doing. On the other hand, when working on something I have good intuitions about, I am often correcting the model's assumptions and it back-tracks what it told me. Unfortunately, I cannot do that comfortably with topics I don't have good intuition about which limits my confidence in "if this is the right direction to pursue."
As someone with a PhD in CS focused on NLP (I started my PhD in 2018 just as Transformers were introduced), and with a strong background in distributed systems owing to the fact that I was a lead developer of an MMO before starting my PhD, I can definitively say that any surface-level understanding you get by interacting with an LLM, is just that: surface level.
If that allows you to target your deep dives better, then great. If instead your deep dive into a topic is purely through prompting an LLM, that will almost certainly end with little functional domain expertise.
The absolute best experience you can get is by trying, failing, then improving upon your past failures. Remove that friction at your peril.
Counterpoint, I think this is true for some archetypes of people, but certainly not everyone. I personally use it like the socratic method. I am an intermediate user, I spend a ton of time with LLMs at work and personally, both prompting and letting some crappy agents try to automate boring work. I primarily use Gemini and ChatGPT models, along with some Chinese smaller weight models (eg qwen) locally.
If you treat the model like an excellent bluffer, it has never been more fun to challenge a model. To me, there is something deeply intellectually satisfying about "proving" it incorrect, and I like being deeply critical of what the model spits back out. I find that refinement process (with the constant sycophancy turned down in the system prompt) creates a really good loop of critical evaluation that would be hard to get in anywhere else. You can treat it just like the Socratic method, but instead of a benevolent teacher, you get a probabilistic bullshit artist. Lots of fun, highly recommend.
My two modes of using LLMs has been to try it for 1) natural language search queries where traditional search engines have failed and 2) occasionally as a sounding board using the socratic method.
Inevitably, it fails frequently at both. Any "reasoning" it is doing is merely rehashing ideas that someone else has already posited. This helps some of the times, but the vast majority of the time it just chooses a biased perspective (frequently the most common) and then regurgitates tired old talking points. This contrasts greatly to speaking with others who often have more intuitive notions that tend to be less polished and rote.
I'd love for LLMs to be better sounding boards, but so far they fail miserably far too often for my tastes. To each their own though.
This, I will use Obra Superpowers brainstorming skill to propose/refine a few viable solutions for a feature or bug I'm trying to solve. After it asks me clarifying questions and presents a spec, I will say "well what about X or Y". The I'll run the grill me skill on the spec to tighten it up, clarifying any assumptions made.
I find it to be a really tight loop and results in very high quality code at a high velocity.
> If you treat the model like an excellent bluffer, it has never been more fun to challenge a model. To me, there is something deeply intellectually satisfying about "proving" it incorrect, and I like being deeply critical of what the model spits back out. I find that refinement process (with the constant sycophancy turned down in the system prompt) creates a really good loop of critical evaluation that would be hard to get in anywhere else. You can treat it just like the Socratic method, but instead of a benevolent teacher, you get a probabilistic bullshit artist. Lots of fun, highly recommend.
Yes, but eventually the intellectual whack-a-mole gets tiresome unless you get really, really good at simultaneously cornering it and not letting it concede to your point.
> If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
So, as long as you are not under time pressure (which you in some degree courses unluckily are), there is simply no need to "speed up" any homework assignments.
If, on the other hand, LLMs help you with making much faster progress in understanding the subject that you study (which is only loosely correlated to homework and tests), I guess it's fine to use them. Just always keep in mind that very often the pain of attempting to understand the topic on your own often makes you smarter - something that you will miss when you take an "LLM shortcut".
> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
This is probably not true for majority of people. Most go to school because it is mandatory, pushed by parents and society, and university gives you credentials and better job opportunities. Homework and tests are a way to get a number grade on 'how well you memorized something', it doesn't really measure a deep understanding of the topic.
> You go to a university because you are deeply interested in understanding the subject that you study.
Echoing the other comments here, at least in the US, this is generally untrue. I went because my parents made me, because the choice was that or get kicked out of the house. It was beaten into my head since I was in grade school that "people in this family go to college" and "you can't get a good job without a college degree."
I hated every moment of it and I was glad to take my BSc and never look back once it was over (University of Houston, c/o 2000). And, indeed, without the degree I wouldn't have had the jobs I've had.
But I didn't go because I was "interested." I went because it was an effectively mandatory life-path objective. I'm very happy for you if your lived experience is different, but it is also—at least in the US—both extremely uncommon and extremely privileged.
> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
There is only one classmate in my class who came to study CSE because they are interested in CSE. And since we all enrolled after AI became somewhat good at everything none of them know how to code.
After two years of study I had to explain someone how to swap two number by drawing boxes. This are the things you learn in the first week if you're interested in programming.
My point is very tiny percentage of people study something because they're genuinely interested in that subject.
> You go to a university because you are deeply interested in understanding the subject that you study.
I don't think I've met anyone who fits that description. The ones deeply interested in the subject would likely skip college anyway if not for future economic prospects.
> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
I think this was true a long time ago. Perhaps with LLMs this can become true again in the future. But definitely that was not why I went the first time, nor most of my classmates. (Second time I did post-secondary, sure, 100% -- but I was almost 30, not an average student)
> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
Some students do not have this privilege and implicitly see university as first and foremost a funnel into a paying career.
> You go to a university because you are deeply interested in understanding the subject that you study.
This is a bit of a naive or maybe affluent take? Like, theoretically, I agree. And I myself was curious. But most people, by and large, are going to university because they know they need a degree to get a job, unlike their parents or grandparents. And even "the degree" is quickly becoming devalued in this current AI age.
I would guess that if all basic needs were met through UBI, the fraction of individuals going to school would drop and the makeup of subjects they pursue would change. Probably more cooking and art classes and less stem. Although, if UBI existed and AI did not, we'd probably see more educated individuals in the first place so maybe there would be an uptick in stem attendance and general curiosity in such a utopian world.
> You go to a university because you are deeply interested in understanding the subject that you study.
You must come from a wealthy background because what you described is far beyond the vast majority of people's means - at least here in the US.
Most of us go to college because it's the only reliable way to get a tollerable job that pays well. Only a few of my college courses aligned with my interests. The rest were just the price paid for the degree.
> If, on the other hand, LLMs help you with making much faster progress in understanding the subject that you study
My experience is that they uncomfortably do both. You can "understand" something conceptually quicker -- like you have a new brain-muscle-thing that lets you cut through the hard difficult tedious corners to get to the meat of the matter.
But then you also can become reliant on it, and have difficulty doing the mechanistic rote work of working through it yourself.
Like the really big powerful calculator that it is, really.
LLMS didn’t invent cheating just made it easier. When you cheat you’re the one who cheats yourself because the point of an education is to learn, not complete the assignments and get high marks on tests alone. No one benefits and no one other than you is materially hurt by cheating, but you are absolutely the one who is hurt.
There’s no way to learn than to force the brain into adaptation which it is resistant to do through challenge and stress, just like your muscles. Similarly you can’t play e sports and get into physical condition any more than you can use LLMs to do your homework and learn.
It’s going to be a hard adjustment for a lot of people to recognize that letting the machine think for you is as healthy as smoking brain cigarettes.
The smart student uses the LLM as a proctor or provide challenges and feedback on attempts rather than an easy button. They make great tools for learning if they’re used as an adversarial or editorial tool. The future belongs to those who work to use the tools in ways that make themselves more efficacious, not those who use efficacious tools so they don’t have to work.
>The smart student uses the LLM as a proctor or provide challenges and feedback on attempts rather than an easy button.
Yeah, this is how we used wolframalpha for Math as students. Whatever we had to do, we did it ourself as a group of three. Afterwards we checked with Wolframaplha to see if we were correct. If there were any difference between us, we went line by line to find where the error appeared.
It was helpful, because we did it ourself, but because the work was graded, we had the security, that it is not a total failure.
To say that students don’t benefit from getting good grades using LLMs is incredibly naive. Learning is only about the third or fourth most important “benefit” for students, after getting a degree, getting good grades, and making connections.
The problem with AI in an educational setting is when one is graded versus their students on things and things genuinely depend on those grades. Group projects also force those willing to do things without AI to go along with others in their group who'll use it regardless.
But I like to add artwork to my presentations. My artistic skills have not advanced beyond 2nd grade. So I'll make a line sketch, and give to AI to "fix" it.
The results are nice and I use them.
I have no interest in learning how to do art well myself, so using AI for it is appropriate.
When LLMs and ChatGPT first came out, it struck me as obvious and dangerous to a deep thinker or a knowledge worker the answering capacity. So, from my initial use I did not ask them questions, I have always "done my own work" and then asked the LLMs to criticize that work. This has been an exponential ladder of learning, and my cognitive growth is personally noticeable. I'm not hesitating to scribble out calculus and work it out, as I need for my work, where in the past I'd have found some other way because I felt uncomfortable with my tip-of-my-tongue calc skills. Don't ask AI, do your own work and ask for criticism, and them improve your own work yourself. This creates a learning ladder that you will climb.
That's nice except when you work somewhere where more and more developers are pushed to pump out slop generated by AI as fast as possible. So far I am not there yet but I have plenty of friends in the industry who are basically 'not allowed' to code manually anymore.
> many of them can no longer brainstorm, code, think deeply, or write
I believe this is the real crux of the issue. We often turn the target to things like "Can johnny Add, Read a book, or recite dates" which are only proxy measures for important things like "Can johnny solve a numerical problem presented to him, can he synthesize information, or can he think critically about what is occurring around him?" .
If students use AI to accomplish goals I do not see it an issue. If they cannot figure out how to use tools, or what their goals are-- that is a major issue!
An analogy of my point is that I don't want to focus on cursive in the age of computers keyboards, and I dont want to focus on abacus skills when a pocket calculator is like $5.
If students are allowed to use AI to accomplish their goals, then I think the real question is why should they go to an expensive university for four years to learn how to ask AI to do something?
We are already remote sensors and manipulators for the corporate and economic structures we operate under. You can't see it, but we are ants in a superorganism.
More evidence of the philosophical concept of 'technology is a life form.' Humans would be the perfect host, at least for the time being. They are certainly a willing host.
They stopped requiring SAT and ACTs in order to get a student population more representative of the population in general. This obviously allowed students that were not prepared for college into the system.
If you do well in your math SATs you'll likely do well in math college. SAT scores and college GPA are highly correlated. No idea why anyone thought it was good to ignore probably the strongest signal of success in college.
> Many of them can no longer sit quietly for even 30 minutes just thinking on their own
Plummeting attention spans has been a trend for much, much longer than LLMs and is more the result of constant digital interruptions and these days overwhelmingly social media and doomscrolling: https://www.apa.org/news/podcasts/speaking-of-psychology/att...
The effects on children have gotten most of the, err, attention, but the effects on adults are no less deleterious.
In 2002 I spoke with a lecturer in the humanities and he told me about how nobody was learning French at university level (in the UK). My own course had been cancelled due to the cost of teaching it, and the era of 'easy degrees' had set in during the early 90s.
Before that, I also noticed the decline in newspaper readership in the 80s.
It is easy to blame this general decline on the latest tech (or moral panic), whether that be LLMs or even the existence of the internet, however, the trend in dumbing down has been going on for decades.
In the context of a declining empire and financialised economies, this makes a lot of sense.
I've been wondering if there would be a benefit to inverting how we teach subjects now. Previously we would teach from the bottom, and build up. Semi-colon goes here, curly brace goes there, and then build up to architecture, systems, etc.
But this doesn't seem to make sense when someone comes to a topic with an LLM in-hand. They need to know high-level techniques, architecture, best practice, etc. As they pursue the topic they start to get down into the details, although probably never learn to do it fully independently.
I quite like this view because it paints a somewhat optimistic way forward from where we are now.
You can ask LLMs about high-level techniques, and their answers will usually be good enough.
What you can't get from LLMs is the taste and judgment, which you can only obtain by having a strong CS base and coding manually for years.
High-level techniques were never a problem. You could Google tens of articles on this topic. They are useless too, it's like learning how to drive a racing bicycle from reading a book. Sure, you will know a lot about nuances, but you will fail miserably when it comes to a real race.
I can't speak for other disciplines, but for math and CS, both with a really heavy focus on abstraction, the final result of learning is to build a nice intuition on top of the abstractions we find useful/expressive. And to build the intuition, the old, usual, and perhaps the only way is to see and practice a lot of concrete examples, after which the motivation of building some abstraction can be understood, and after which the abstraction itself can be fully grasped.
e.g. The "group" abstraction requires one see a lot of int, polynomial, modular arithmetic etc. before knowing why we want such a thing. It's unskippable.
This idea sounds good at first, but if you look closer, it would just make workers, not experts who really understand. What we could do, and already do, is tweak the learned abstractions. In our field, it's easy to see: most of us first learned about computing abstractions, not how processors actually work, or started with Java, not assembler.
Plus, you can't teach math from top to bottom.
> As they pursue the topic they start to get down into the details, although probably never learn to do it fully independently.
It's hard to claim one has mastered a subject without independent command of its fundamentals. A less charitable take on this future is that students only learn to hand-wave answers and correspondingly cannot evaluate statements beyond "sounds about right".
I keep trying to convince people that English majors and Philosophy majors will benefit the most from LLMs. English majors in particular, have been trained to be VERY exact in how they word things.
That awareness of how to structure the English language, it will benefit those who use LLMs.
Then again, maybe someone will just make a LLM that’s built to turn poor English and poor reasoning into excellent English and excellent reasoning. Maybe this is just a technical puzzle that needs solving.
I don't think you can learn high level techniques or architectures without first understanding the basics first. This means boring boiler plate coding.
Yeah. I have no doubt that I would have used LLMs “just this one time” to help with problem sets or papers when I got behind or wanted to do something else.
I have observed this in myself when I began to over-leverage AI in my workflows. I've since become more deliberate with what kinds of tasks I will use it for, although I still slip up.
With writing:
Things like brainstorming a plot line for a book with a custom GPT or Claude project that has all of my prior books in its knowledge? Works great.
Things like asking it to write a paragraph or chapter for me - I can rapidly feel my own writing skill, motivation, vocabulary, and ability to grasp/remember the resulting plotlines deteriorating. I don't use it for that anymore.
With studying:
I've been taking a couple of evening uni courses and the thing I found so great is that I've been forcing myself to think through the problems, and take my own notes in every lecture. I may then still get ChatGPT to help explain and reason through some of the concepts with me. And I have it review and 'grade' my assignments. But I refuse to ask it to start drafting answers.
With programming:
This one is tougher. When I am not very personally invested in a problem or codebase it becomes too easy to offload more parts to Claude, and when the company encourages 'vibing' to speed up velocity and you're reviewing and writing a higher influx of lower quality PRs, investment goes down. I still sometimes catch myself committing solutions I only _mostly_ grasp and the rest is hand-waving. A big part of it is a work culture thing.
For my own projects I make sure to understand and have a back-and-forth with the planning agent for each task, or write the first plan myself to go off of. When it comes to producing the code, I have to admit it is much easier to properly review parts of the codebase I am extra interested and knowledgeable in (backend in my case). The frontend I'm less well versed in and also admittedly less interested in, so I do sometimes fall into the trap of "Ehh it works, just commit it" with the goal of doing a thorough quality pass before actual release.
With all of the above, I can feel my ability to think, plan, reason, focus (and my vocabulary) suffer if I go over the line too much into agent offloading. For me keeping that balance is as much about maintaining my own long-term brain health as it is about producing good output. I imagine younger people growing up with AI today won't even know what that more capable (in my opinion) brain state feels like - to them, the AI-using brain will be the norm.
The place I've come to with AI for writing is to have an idea for a chapter/article/etc, which I take to AI, and tell it to either ask me a bunch of clarifying questions, or try to blow holes in it/challenge it. I'll keep talking to AI and answering questions/handling challenges until the AI runs out of steam, then I'll ask the AI to write out a condensed outline with all the pertinent details of the conversation.
Once I have the condensed outline, I'll re-order stuff, clean it up/tune it up, then do the final writing. This keeps my voice and logical train of thought while avoiding blank page syndrome and some of the organizational mess of condensing notes into an outline manually.
We’re in a world where LLMs are basically going to be extensions of how we think. An additional thing we use to do a lot of thinking tasks.
As a piano player, it’s important to work hands separately. Sometimes your right hand will carry the melody and your left hand the harmony, sometimes vice versa. Sometimes there may be more than just two “voices”/melodies/lines between your two hands. Even as a very good (as in getting paid to do it) sight reader, I learn a lot working all the voices/melodic lines separately.
Singers do similar things like singing only the vowels to keep themselves in the right placement. Learning handstands, you have to work your wrists, rotator cuffs, core (which is many things), etc. separately. Yoga, Pilates, and running also help us learn to break problems down this way.
Anyway, all that to say: If LLMs are gonna be a natural extension of how we think, we need to understand what parts of problem-solving LLMs are good for, and what parts our brains are for. The nice thing about working these bits “separately” is that one side is done for us. So we just need to consciously practice using our brains.
As programmers that means, maybe we conscientiously practice writing things ourselves sometimes. Remembering that this even if this sacrifices short-term “velocity” (whose measurement is problematic, but I digress), it preserves our long-term ability to do good work. And I think any of the above physical/artistic practices (or countless others), worked in these ways, will help reinforce this entire mindset.
I think kids of the coming generation will be sharply divided on their ability to conscientiously practice things separately. It’s been happening, but I suspect LLMs will accelerate it unless how we actually teach kids can catch up.
> We’re in a world where LLMs are basically going to be extensions of how we think
If that's the case then we're in trouble based on my experience. This week I've been using ChatGPT to help figure out some old linux platform that I need to resurrect. It's very good at quickly searching and surfacing relevant information online, and that's helpful, but if I did not have a lot of experience at linux administration to be able to see where it was suggesting the wrong thing, or initially dismissing the right thing, then I'd just be thrashing.
The LLM is helping me because I know what I need, and it can search and read faster than I can. But it's not really very smart.
> An additional thing we use to do a lot of thinking tasks.
Which is to say, an additional thing you're going to be forced to pay a lifelong tithe to a trillion-dollar company in order to do a lot of thinking tasks.
I dunno, I used wolfram alpha a lot during calculus classes. However my uni didn't require any homework assignments to be done and they did not contribute to grades. Only the exam mattered.
Maybe the problem is that doing assignments contributes to your grades? The answer from wolfram alpha wasn't so much to get the homework done, but to understand how I would be screwed in the exam.
I don’t buy it. Properly leveraging LLMs to generate stable and extendable systems is mentally exhausting (i.e. highly demanding of intelligent thought), especially given the poor quality and churn within the harness ecosystem.
Now, if you’re creating trivial, unstable, or nonextendable systems maybe this doesn’t apply. And maybe I have long overestimated the work that SWEs have done.
i use claude a lot and i find that it is best applied in domains in which i am already a master. I tried applying it to domain's im unfamiliar with and i found that i produced stuff but as time went on i understood what i produced less and i almost felt like i do after binge watching a netflix show, 2 weeks later i barely remember any of the details. I wonder how much you need to "do" to learn and remember. LLM's give you a shortcut to doing and so you probably aren't learning either. It's like when you watch a professor write a proof and it makes sense while listening to the professor but at home you have difficulty deriving it. LLM's give me the same sort of feeling. I think the way forward is still going to be doing things manually to learn and using LLM's once you've mastered an area and people who don't understand this fact are going to slowly descend down a hill and forget how to depend on their own thinking.
As much as I hate to admit it, using agents for too long makes me less able to think for myself. I am dedicating 30 mins to 1 hour everyday writing and thinking without LLMs.
> I am dedicating 30 mins to 1 hour everyday writing and thinking without LLMs.
Before you did this, was literally every hour of your waking time spend thinking about LLMs?
I don't think I could do that even if I tried, and I spend all my development hours with agents, but during meals, showers, walking the dogs, enjoying a coffee outside or whatever, naturally I get time to think about other stuff, sounds out of the ordinary (to me at least) to have to dedicate 1 hour to not think about something. Reminds me of when I was addicted to amphetamines way back when.
I wonder if AI or something else changing (developing anxiety, etc) has made the pay-off of the degree less certain. If you're confident that your years of effort will pay off, it's probably easier to see it through. If you're worried that AI will wreck your industry before you hit the workforce, maybe the equation changes and you're more inclined to gamble with shortcuts?
Yes, and this is going to hurt everyone. If everyone knows that you can skate through a college degree without doing any work, it is not going to have much value as a credential.
I totally agree about school-level homework: it was many years before my pre-frontal cortex developed enough that I could have forced myself to do the work.
That said, though, one thing I don't understand about the heavy users of AI in academia and software development is that the thinking and coding is the fun part. And that's the part so many people seem to be so keen to automate away.
I'm right there with you. The thinking and the coding is the fun part. I'm pretty relieved that all of this is happening near the end of my career. To me, AI is just not fun. And constantly signaling how productive I am and having to show "my value" is exhausting. This is only my subjective experience, of course, but in many ways the world seems like the fun is getting sucked out everywhere, not just from AI. Like the type of people that become managers are taking over everything.
LLMs have killed my facility but not my knowledge.
I can still read code and write it, I just need to look back at docs a lot more, when I used to just know things. I also have to sit and try to recall how to do things and what abstractions are involved more. I also have more "writer's block" when starting with a fresh program/document if trying not to get AI to seed it with a baseline implementation, where I have to sit for a while thinking about what I really want to build.
I don't understand why people take shortcuts in school. You pay a LOT of money to be there to learn. Taking shortcuts seems completely counterintuitive to me.
- Time is a scarce resource. Students do what they can to learn what they can, but if they're under the gun, they'll take the path of least resistance to make it to the next day (totally not like the business world, right?)
- In the interest of having well-rounded students, a lot of degree programs include subjects the student didn't want to sign up for, but have to. Even in something like CS, I knew a lot of people who liked the hardware side of it, but didn't like the software side and vice versa. So I can imagine a student justifying taking shortcuts that way.
- Psychological reasons like wanting to protect their ego. Maybe they had always done well in school and are now struggling, but don't want to ask for help, so they think why not just take a shortcut here and promise to do better next time, etc., etc.
A lot of people view it, rightly or wrongly, as paying a lot of money to earn a degree that opens up certain opportunities, while learning is secondary, so minimizing effort is worth it.
And to some people, it's not even a lot of money.
In many ways, schools are just the modern day peerage system.
before AI was around we blamed Covid for doing this to us, and now we blame LLMs... and before that we blamed social media. I'm pretty sure this downtrend has been happening for decades.
Yes, I can churn out a lot more stuff as can most of my peers. Experiments etc are all way faster to run with coding agents. But I think the overall creativity and originality is a lot lower. I think this is what many people are facing, if you don't use LLMs your short term productivity is worse.
They're incredibly more productive. LLMs are amplifiers, so where they'd have branched and tried out N things, they can easily try 5N pathways of RnD. LLMs are extending the frontiers of science fast -- math -> phy -> chem -> bio in that order.
This is all assuming tests measured anything valuable in the first place. In my experience standardized tests were always flawed and most of my peers knew shit about the subjects they passed in top % a year after. If AI breaks the current education system that's a win in my book.
> Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well
I noticed this before LLMs became a thing. It was by accident. We had a team of programmers. All decent at what they do. The management said 'hey you want to learn another language we are going to be using it for these upcoming projects'. So we set up a self learned at your own pace class curriculum. Maybe 10-20 hours of school work if you sat and really dug in. Maybe 3 to 4 hours if you breeze thru it and do not care much. We set up weekly check-ins doing about 1 hour a week. Easy. Watch a 20-30 min of vid 20-30 mins of do homework come to check-in and talk about what you learned and help others if needed.
Now this is where I was disappointed. The first 'class' was 40 people. By the last there were 3. Those 3 I noticed always are the ones who dug in. The rest wanted a proctored classroom and someone to tell them what to do.
Actual genuine curiosity is rare I think. We have a lot of people who are decent at what they do. But do not really care about it. IF you do not care you are going to just push the button and get the answer.
I could see myself dropping out even if I was interested in learning. I'd suspect that the time spent would end up with me needing to stay late to make up for it or being penalized in some other way.
I'd argue that this is an adjustment period that society has to go through. The way we are using electronic devices today, in some years it will probably be looked at like smoking cigarettes. And I'd argue that a lot of the "decline" is due to a shift of skills away from things that mattered more in the past toward other things that are not measured/perceived by the older generation.
Interesting analogy. I believe regarding addictiveness they may be compared.
> a shift of skills away from things that mattered more in the past toward other things that are not measured/perceived by the older generation.
Do you have any ideas what these things might be? As someone in his twenties, I’m sometimes saddened by observing that some of the skills I acquired over a long time (e.g., writing, coding) may become obsolete or won’t be respected anymore just now that I‘m finally getting good at them.
Eh, I think it's less like a cigarette and more like the car. We're not going back. Americans are famously less healthy the more car dependent they are, and now people walk/run as an explicit task to be healthy. People will start going to a "thinking" gym, or engaging in additional manual mental activities for sport, like we do with chess today.
> I'd argue that this is an adjustment period that society has to go through.
I used to think like this until social media proved there are some tech innovations we just can’t adjust to. 10 years ago you would’ve never caught me supporting any sort of age based social media ban. Now? I don’t think it goes far enough. Fake news (actual fake news) and misinformation has only gotten worse with it as well. It’s so destructive.
I think it varies tremendously from one role to the next. I'm a senior software engineer and LLMs, the way I'm using them, improve almost everything I do. I use them to write most of my code now, but first I spent twenty years writing code before LLMs came into existence and second writing code is like 5% of my job. Most of my job is research, investigation, and architecture. I treat LLMs just like a junior engineer. I give them clearly defined jobs that I could do on my own just fine, that I already spent years doing. The problem here is that students are using LLMs to automate everything BEFORE they become proficient at it themselves. Letting college students use LLMs for homework is like letting kindergarteners use calculators instead of counting on their fingers.
You cannot tell me that letting anyone do something for you does not affect the skills that you outsourced, unless you are some sort of a superhuman.
As an example, I have been drawing portraits for quite a few years now, and whenever I go on a hiatus and come back after a few months, I can notice my skill not being anywhere close to where it was before I stopped using it.
Sure, after 2 or 3 portraits they mostly come back because of the previous experience, but skill rust is a real thing, and if you think your coding skills are the same because you used to code 20 years but haven't coded for some time, you are probably just lying to yourself.
On the contrary, with the amount of times I went to ask for help and was failed pedagogically, plus not being able to afford tutoring like my peers had, I think access to an LLM would have genuinely boosted my grades.
I still did well, but I had gaps for which there was no help outside of the internet available.
The risk or difference is that tutoring helped people learn which they can use to do the work, whereas with only one or two different words an LLM will do the work (that proves you have learned) for you. A tutor has limits, but an LLM needs to be asked to set limits. And especially younger people are less likely to "punish themselves" like that.
I recently switched back from a Tesla to an older car without permanently having a map visible. Suddenly my brain has to think about routes again and it definitively feels like my brain has to put in more effort again to handle it.
This likely varies person by person or the way people adapted AI. For me AI replaced the boring part of writing code, but has not replaced the fun part of thinking about code and problem solving.
I used "AI" in the 2000's to increase my homework assignments, and to correct them.
As in I wrote code to generate random exercises, with solutions, using many tricks, to get myself hundreds of problems instead of 1 or 2.
Often spent more time on getting these programs right than on the problems. Still did better than the class. Oh and it was AI in the 1980s IBM sense. Ie. it was based around a python version (which I wrote) of a LISP math system based on maple. I even attempted (and largely failed) to rewrite it in C++.
Even attempted to have my homework read to have the computer correct the actual pages, but I never got convnets to reliably read entire lines (yes, I understand, well now, why a convolution would mostly not realize whether 2 pieces of text are on the same line or not and so get very confused if you go deep enough for recognition to work well)
Yeah, it's a scary thought. I feel the pull of it every time I'm stuck on a code problem that I don't want to search solutions for and hand-code... and I also feel myself wanting to reach for the crutch of an LLM when I just have something boilerplate and easy to do. It's incredibly tempting to just ask the question and have the "thinking" done for you. Until you have actual skin in the game and realize that it doesn't reason, and its "thinking" is utter shit. Then it's like: you got addicted to cigarettes and now you have to quit, because this habit is poisonous. It really does lead very quickly to cognitive decline if you rely on them, or even think about asking them while you're writing code.
> Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well; many of them can no longer brainstorm, code, think deeply, or write without an LLM present doing 90% of the work. Many of them can no longer sit quietly for even 30 minutes just thinking on their own, which is a required skill for producing original thought.
This was my experience even pre-LLMs though (about my own PhD thinking skills too). I blame the amount of random stuff work now involves more than LLMs.
No, that's the quality of candidates. I wish I was joking as a PhD holder for only 15yr.
A lot of skill of
is getting bled into the private sector because getting the PhD in a lot of regions doesn't mean the step up it used to. A lot of that comes from awarding them to layabouts doing "a gender critical analysis of ...".
Industry doesn't how/what/why they just wanted the 3 letters as a performance barrier to hire competants.
You make good points here. But I want to point out some issues that I have with what you are saying, because I see a few assumptions that I would not myself make.
I graduated from RPI with a degree in Management and a concentration in Information Systems. I began in Computer Science, and didn't like it because RPI CS at the time was loaded with professors who were mathematicians who had transitioned over to CompSci and because the 100 and 200 level courses were excessively math-heavy in my view.
Since this was the late 80s, there may not have been an easy way to teach B.S.-level computing without it being heavily math-based, but I digress.
No matter what degree we achieved or what work we ended up succeeding at, we have a tendency to look back at people rising in the ranks below us, see differences in their experiences and struggles, and say, Look! That is evidence of a lack of rigor or a lack of understanding of fundamentals that we had to learn in order to succeed.
The only thing is that some of what we learned to become successful just isn't necessary to be learned when we learned it.
I do a fair amount of low-level software engineering with Claude Code now that was above my level of understanding of data structures and algorithms because I never took those CS courses at RPI because I switched to Management IT.
But as someone who could be described as a solopreneur at some level, my new system designs reach a certain level of complexity or code maturity, and I hit problems that I would not hit if I had more understanding of data structures and algorithms.
So-- I end up having to learn aspects of those disciplines at that point, rather than before I actually needed them.
I run into these situations often enough where I now say to myself, gee, I wish I had taken Data Structures. And I think, could I effectively take Data Structures at this late date and get better at specifying how I want data stored, or perhaps knowing the shortcomings of simplistic database structures that are the ones I end up with initially because of my lack of spec-writing skill?
Aren't many of the less experienced folks who come up now, whatever age they are, going to hit problems that show them their weaknesses in this fashion?
Is the issue that these people will never get jobs because the seniors and managers who are interviewing them will design interview questions that keep people with their level of understanding out of the workforce?
What happens when somebody who sucks at the fundamentals but is really motivated bangs their head against their shortcomings and eventually succeeds in building something that takes off? Aren't those people great assets because they learned some of their critical skills the hard way?
> If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
As a counterpoint, I was once a physics grad student. I didn't finish the PhD because at some point I discovered that I was not going to be the next Richard Feynman and this was too much for my ego at the time. But I think that if LLMs were available, I might have finished.
Part of my problem was that at some point the math transitioned from stuff I understood to symbols and notation that I knew how to manipulate but didn't really understand. LLMs could have helped bridge that gap.
On the other hand, it's hard to imagine I wouldn't have used it for Jackson, etc. but we got Jackson solutions from previous students and the internet anyway. Using LLMs probably would have been more effective, used correctly.
This is also where I had issues advancing in math. For so long I was able to build intuition around mathematical concepts easily. They fit in my head and made sense. I couldn’t understand why my peers were so bad and slow at picking up the concepts. Until my first calculus class where there was absolutely no focus on the intuition or practical utility. It was just formulas for the sake of formulas as exposed by our teacher.
It wasn’t until I was curious enough to learn about calculus outside of the classroom that I was exposed to things which helped develop that intuition and made the calculations something other than just symbols and equations to memorize.
I think this would be fine as an adult, if it meant using LLM to churn out the boring work required of you at a corporate gig, to spend more brain cycles on something you actually want to work on.
The problem is that it sounds like many people are just using it for everything.
> For adults the cognitive decline won't be as measurable since there's no exam
I think this is true of every affliction that adults criticize children and teenagers of
I’ve been out of university for a very long time, and I took a community college course and for the first few sessions I couldn't focus or sit still at all. Fortunately I knew that was abnormal and how to conform to a prior version of myself, but I don’t think children have a frame of reference.
>Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well;
tomorrow most regular people's thinking skills will definitely be weaker than those of the LLMs of tomorrow. And physical skills in most cases will be weaker than those of the robots. That leads to the question - what would most people do?
I've got mixed feelings on AI assistance. I'll relate 2 anecdotes.
1 - When I was in grad school (before AI), we had to use Canvas for a class. One day, I got an obvious spam/phishing email in the internal Canvas system. It was so strange. The writer just would randomly hit the capslock button and keep typing away, no salutation, no signature, just a real mess. They were asking for a particular professor to come to their house to teach them about ... something? Again, real strange.
So, I email IT and say 'Hey, somehow a spammer got into the system, do your thing'.
They email back and go 'Nope, it's a student, that somehow managed to CC the entire system, sorry about that'.
Dear Reader, the message was pure garbage. Literally, it looked liked it was written by a 3rd grader without any shame. [0]
I happened to know the professor of the class. So later on, I talked with them over symposium coffee about it. They said that they remembered that particular email because of all the IT back and forth. It was for an upperdivision class in the Engineering department. The email itself was not particularly notable otherwise. In that, they saw such emails all the time, in terms of quality. This was a top 100 ranked (whatever that means) university, by the by.
Shocking.
2 - My grandfather was an officer and a mechanic for the USAF. A bit of an odd combo, but he was partly responsible for instituting many preventative maintenance checks and protocols, novel in those early days of the AF. His aptitude and memory were quite sharp for many mechanical things. Until the strokes from decades of smoking caught up, he could tell you exact measurements and torque values for a variety of airplane related things (I can no longer remember what exactly, the memory skills did not transfer to me).
I do vividly remember standing in that light blue garage of his and him all but yelling at me once. We were looking at the brakes on an old car he was 'restoring' (getting away from Grandma for a little bit). He pointed at the old drum brakes on the axel.
He asked me how tight the pads should be on the inner rim of it.
I had no idea.
So he asked where I might find out.
I figured I'd ask him.
But what if Grandpa wasn't there?
We'll I'd have to look it up somewhere (they had no internet).
Fantastic. Now, what about the next time you're working on the brakes?
Well, just make sure that the pads are at that spec.
And that when Grandpa hit me with the nugget of hard won wisdom: No, you look it up every time. Because these are brakes, and if you are wrong then they might fail, and they might fail when the driver has their whole family in the car at 100 mph. And then because you were lazy, half a dozen people die.
---
These two times stand in my head when it comes to AI.
For the first one, yes, AI would be such a boon to that very clearly struggling student reaching out for help. It would get them back on the path to the real struggle of getting their degree. That level of assistance would be like a wheelchair to a paraplegic.
For the second anecdote, AI is condemning people to death. Using it in life critical situations and care, letting it hallucinate or skip over critical values, that's a recipe for disaster.
Where do we set the fine line of using AI and not? For brakes and X-ray machines, obviously not. For helping kids learn to write emails correctly? Sure, sounds great.
Unfortunately, I feel the old adage about regulations is going to be true here like it is with every new technology: The rules are written in blood.
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Are you saying you leave it up to the LLM to judge whether your idea is good or not? Are you even human anymore?
(I am not saying LLMs can't be a good tool in evaluating ideas. To me, it sounds like you're firing off ideas all over, letting the LLMs judge what's good and what's not. Insane.)
When reading and writing became prevalent, the ancients bemoaned our reduced facility to memorize long texts. Are we now “less smart” because of that technology?
The likely 'real' reason is hidden in one paragraph within the article and has nothing to do with the implication of the eye-catching title: "Both Garcia and Ranade have joined more than 1,300 UC faculty in signing a petition calling for the reinstatement of ACT and SAT standardized testing scores for STEM admissions in the UC system. The petition and its accompanying open letter detail similar concerns with students’ mathematical preparation."
Around COVID times many top universities experimented with removing test requirements from admissions, under an argument largely related to equity. It's been a failure everywhere, with many, if not most, universities already reversing it. As Yale put it, "Yale’s research from before and after the pandemic has consistently demonstrated that, among all application components, test scores are the single greatest predictor of a student’s future Yale grades. This is true even after controlling for family income and other demographic variables, and it is true for subject-based exams such as AP and IB, in addition to the ACT and SAT." [1]
That link is for an archive because that page has been removed. That's because they briefly experimented with a new 'test flexible' strategy where they allowed students to submit test scores or not, but then scrapped that altogether and went back to simply requiring test scores.
Berkeley chancellor told students to vote for 2020 California Proposition 16, which would've repeated 1996 Proposition 209 that banned race-based admission in public universities. Prop 16 failed. Subsequently, Cal started ignoring SAT/ACT scores. I have to think this was their alternative way of taking fewer Asian students, who average highest on that. Soon after I got an email from the same chancellor praising the change for bringing more racial diversity. The email included before and after numbers where % Asian decreased and all others increased.
They could have easily made test scores a pass/fail per program and not weight higher scores for admission purposes. It achieves the goal of ensuring students have requisite knowledge for the program while not favoring students who are able to ace the test.
Or, even better - just expand programs so they can accept more students who pass the test. This would probably improve diversity without artificially restricting access to highish performers.
It varies by school. I went to a (low ranking) state engineering school and it was guaranteed entry if a prospect met the following criteria:
- Had high school diploma (or equivalent).
- Resident of the state for >6 months (student or one parent).
- ACT score of something like 21. With provisional admission granted to students with scores below, until they completed all first year engineering courses with a B or better.
So likely they just dropped the concept of provisional admission. All that did was open up classes for registration a week later to ensure other students were able to get their preferred class openings. Provisional had to take the scrap classes, like the four-hour, once a week Calc class on Friday night.
But do these universities not have math placement exams? Not for admissions but just before you register for your first semester classes, a 30 minute math test should be a straightforward preventative measure. I did a test like this, I assumed they were pretty universal.
Memorize trivia and formulas, regurgitate trivia and formulas. This summarizes my experience with our system of education. Yale saying test scores predict performance reads to me as, “students’ history of being able to regurgitate trivia and formulas in high school is the lead predictor of their ability to do so here.”
> removing test requirements from admissions, under an argument largely related to equity. It's been a failure everywhere [...] among all application components, test scores are the single greatest predictor of a student’s future Yale grades.
It reads as though you tried to use the quote to support your conclusion that "it's been a failure", but the quote and the original rationale are optimising for different things. Something can be a success in improving equal opportunity while still leading to worse grades.
Or to flip it around: we could say admission testing "has been a failure everywhere" because it biases admissions in favour of certain demographics. But that wouldn't really be a fair assessment because being free of demographic biases is not the purpose of admission testing!
CS Professor here: just yesterday I did the discussion of a course projects' (Parallel Computing), and one of the three groups that I did yesterday have clearly gone the ChatGPT way. They couldn't even understand the choices the LLM made regarding the architecture, etc. The way to "catch" these students is similar to what we did in the past when students copied from other students which is "to give them rope to hang" - ask for clarifications until they follow unintended paths that lead nowhere.
To fellow professors, when you're suspicious my suggestion is to appeal to their honesty (like "let's be honest, how much of this code is yours, and how much is ChatGPT's?") and offer some empathy and understanding (like understanding they may had multiple deadlines in the same week, etc.). Nevertheless, don't miss the chance to give them the lesson on how is the correct way of doing things. The way to catch these students is to find the same signs of yesteryear copying from other students (which in essence is what copying from an LLM is, although the number has increased because they found us professors unprepared for the volume).
The other two groups also used LLM but in a high-level and architectural way. They were clearly responsible for the code (even if they didn't wrote it 100% manually) and could explain their reasoning and strategies used to solve the problems.
Me and my colleagues still have a lot of projects to review, and I asked them to keep the score of the number of projects like these, but so far, the score is 1 in 3 (33%).
Everything that provides students with a workflow to think and to try to find solutions to a problem is much better than giving the answer directly! Unfortunately there will always be students that prefer to take the shortcut..
How could we "force" the students to use an LLM that confronted their doubts with more questions? We could tell them to start each chat with a specific prompt (to use the socratic method, etc), but they could eventually jail-break it..
But nevertheless, I like your idea! This is something that a document highlighting methodologies for students on how to use LLMs effectively could/should contain..
Using AI to inform architecture doesn’t seem so different from googling architecture in this case. Architectural patterns are mostly well understood and well documented these days and are something that you could piece together via Google search pre AI. The thing that AI brings to the table that wasn’t google able in the past is code generation. Previously you had to understand the architecture patterns to implement them yourself, but now the AI can just do it for you.
> Would you have accepted them cooy-pasting code from libraries together to build their project?
Yes, if they are "responsible" for the code delivered, where responsible means they understand the code, the architecture, the decisions made, etc.
In this case, the students had to invent multiple strategies to solve a specific problem. The "successful" groups did a mix of generated and hand-crafted code (don't know percentages), implemented different strategies and knew their plus and minuses, could change the code in a timely manner to accommodate some of my requests, etc. The "unsuccessful" group couldn't do any of that.
I'm not anti-AI (and really, what could I do if I were?) since I use it myself, I'm just anti-slop, especially from my students.
But in reality I've been slowly transitioning from group projects (for a subset of the grade) to "practical tests", where they must implement a significant subset of a larger project in a 2h class. Still experimenting though.
This is all so new, and caught us completely unprepared that there's no official university-level policies. Most of us are still navigating the waters and seeing what works and what doesn't work anymore.
I have colleagues that are teaching for more than 30 years, few years away from retirement, who suddenly have been confronted with a new way of doing things. Those are the ones that are still insisting on doing practical projects, etc. I've only been doing this for 20 years, and I'm quite lazy (worked previously as software engineer), so I've moved to those practical tests. I guess that there should probably exist a class or workshop to teach these students how to use LLMs effectively, but as I said, this technology and its implications is quite new.
Personally, what I did was to give them the "lecture" in the line of that they do not understand what the machine has generated, that is not the way a true engineer does, try to do some parallel with things like an LLM designing a bridge and civil engineers building that bridge, and a fatal flaw collapsing the all thing, etc.
In other words, we do not have a formal system in place, it's all talking and convincing them. Obviously it's a big enough problem that should deserve more investment in solutions, but we are all overwhelmed by other tasks. Maybe LLM studios should be held responsible for all these "disruptions" and provide solutions to problems they created! :)
It's a strange thing that as humans, we sleepwalk into every crisis, never agreeing on anything, and then when we're there, we also never agree on the causes. When we ge too the point where we can no longer "engineer" or "science" anything we will spend the next decade arguing that the issue was not really AI, or that if it was, it was inevitable and no one (or everyone) was to blame. Rinse, repeat. Yet we're here, today, looking at the bleak future, and taking yet another step forward.
Do we assume society just self regulates. I think it does, but the cost of letting it self regulate is really really high, with lots of suffering. Is it that we find this acceptable when there is a chance we won't be the first to feel the pain?
People have been warning about AI coming for decades. For better or worse, it's embedded in popular culture, in science fiction books and movies. But that's different from figuring out practically what to do.
It's cultural evolution and it's how markets work, too. You were expecting central planning?
My son is 15 and I use Google Family Link to control what he does on his phone: it's pretty open for the most part (I receive notifications of installs) but Gemini is a hard-ban.
We've spoken at length of the dangers.
He says his pals use LLMs frequently and I suspect that's the reason for their test scores: some of them are in the 20% - 40% range for tests whereas my son is 80%+ because he studies past-papers and answers questions in his revision.
I worry for the future coz you can be sure that the AI providers don't care if a schoolchild is using their LLM to answer the homework questions.
"More than 600 University of California faculty members, led by mathematicians at UC Berkeley, are calling on the system to reinstate standardized testing requirements for science, technology, engineering and mathematics applicants, saying that six years of test-free admissions has not reliably assessed readiness and professors are often teaching middle school math to incoming students."
The book SAT Wars has arguments for and against and the striking thing for me was that some in admissions believe in a concept called crafting a class: the applicants are input into the admissions officer’s artisanal contribution to producing a class that they believe would be good for the university to have.
The idea of a standard bar and so on does sound like it would interfere with such a process.
I always did find it interesting that US notions of anti-racism required treating individuals not as individuals but as racial representatives. It’s a local quirk of the culture of the land, I suppose, that one’s primary identification here is one’s skin colour.
The decision wasn't specifically to drop a standard bar. It was to drop the existing bars because they have become heavily gamed and are far more reliable indicators of your family's resources than your ability or likelihood of success. That was the equity argument.
Unfortunately, the lost signal wasn't replaced with anything. (I don't know what could replace it. It's an incredibly hard problem. )
My daughter was struggling with her Math class back in January. I used Claude to build a tool that allowed me to generate very focused worksheets. The worksheets had problems designed to drill the concepts she was struggling with.
It worked, and it would have been MUCH harder to do this the traditional way.
The tool generates PDFs including an answer key and solution sets that solved the problems using a variety of techniques so I could check her work more easily and we could iterate quickly.
That's powerful. It comes back to how are you using the tool. Are you using it to make things better or to take shortcuts?
>In addition, the guidelines state that “a typical GPA for a lower division course will fall in the range 2.8 – 3.3.” In spring 2026, both classes’ average grades were C-pluses, according to Berkeleytime, corresponding to a 2.3 GPA.
As a Cal alum, I am actually really glad to see they are holding the line on grade inflation. I worked my butt off to achieve the GPA I did, and it would really suck to see my labor devalued if Cal went the direction of e.g. Yale and started handing out 79% A's and A-minuses: https://yaledailynews.com/articles/professors-face-grading-d...
I read the subreddit for the UC I went to. When acceptance letters went out this year there were (as you expect) a ton of questions from accepted students. About 1/3 to 1/2 included questions about how bad "grad deflation" was, asking for comparisons to other campuses.
Unfortunately grade deflation has little positive impact for the students. Medical and law schools often (typically) don't take grade inflation/deflation from a school into account. And almost no scholarships take this into account. If you do have professional school aspirations, there's very little benefit to being at a school with grade deflation.
I doubt that's the bottleneck. UCB's acceptance rate is not high (<5% for CS). They have way more people who want to get in -- qualified kids, too! -- than they can fit. They'd need to burn through that backlog before it started showing up as a signal.
Unpopular opinion: turning public universities into an academic hunger games is diametrically opposed to their purpose for existing, which is to create an educated populace. Intentionally lowering the quality of instruction, as well as deliberately trying to trip students up on exams, is not improving educational outcomes for anyone. People who complain about "grade inflation" have completely lost sight of why public education exists in the first place.
Obviously a balance would be best, but as someone who went to a very grade-inflated school, I do believe that grade inflation gets in the way of education substantially. When you can get through classes with very little effort and understanding and know you will get a sufficient grade, many people will simply not learn the material deeply.
Some of the exams in Berkeley were brutal, but they never felt like trick questions, they did on occasion require a level of mastery of the material which was extreme, but it never felt like someone was just trying to make the questions obtuse for the sake of it.
Even more unpopular opinion: universities don't exist to create an educated populace. People don't need universities to learn, they can read textbooks on their own.
Universities exist as gatekeepers and credentialing bodies. Their purpose is to certify that a person has studied some topic in depth and is an expert in it. They promote education indirectly, by giving people an incentive to study.
A good university is one where anyone with a degree is guaranteed to be highly knowledgeable in their field of study. This makes it easier for anyone who might want to employ or do research with graduates, as there is no need to test their knowledge.
By this metric, universities have failed spectacularly. This is particularly obvious in computer science. Employers routinely ask CS graduates to solve data structure/algorithm problems in interviews, because a degree is not enough to prove that somebody knows this stuff.
Except this is exactly the opposite of turning it into the hunger games. That would be a situation where failure is kept artificially high by high-grading/curve. This is not that.
No one is intentionally lowering the quality of instruction or trying to trip students up. They are trying to get them to pass the same bar that generations of students before them passed fine...
There are 10 different public universities in the UC system and 23 in the CSU system. The majority of them are not difficult to graduate. If you don't want a demanding education, don't go to a demanding university.
>Intentionally lowering the quality of instruction, as well as deliberately trying to trip students up on exams
I was happy with the quality of the instruction, and I didn't feel I was being "tripped up" on exams.
It's not about "hunger games", it's about challenging students to learn a lot of material and learn it well. Again, if that's not what you want, just don't attend.
Writing better exams, even if they're more expensive to grade, and removing homework from grading as far as possible addresses this problem well wherever it's applicable. Senior-level math courses at many universities are already like this: homework is ungraded, or counts for little, and it's possible for students to "cheat" on the homework by copying another student instead of struggling through the exercises. But the students who do that don't learn much, if at all, and predictably fail the exams. Professors warn students at the beginning of the class and tell them how this will work, something like:
> You can always ask me for feedback on your homework and I will mark up every part of it, but you won't receive a grade for homework. However, if you don't do the homework and take your time with it, you will fail the class. My office hours are in the syllabus and you're strongly encouraged to use them. There will be an early exam to give you a chance to know whether you are likely to fail this class before you lose your chance to drop it.
Correctness is harder to adjudicate in some humanities disciplines but the format of these exams is actually not super different from essay tests (when a math professor grades a proof, they're inspecting specialized prose for validity, coherence, persuasion in a way that also reveals knowledge).
When you don't rely on homework for determining whether or not a student passes the class, you make cheating on the homework into the student's problem instead of the professor's or the university's. Students have the right incentives to solve problems for which they are the ones responsible, and they figure it out after one failed (or ideally, dropped) class at worst.
Pity. I recently started a fun activity to rebrush my math my where I tries to solve problems while asking Gemini Live mode for confirmation and suggestions, sometimes step by step.
It kinda was fun, like a very patient professor stand right besides you. It was the one of the best math learning experience I've ever had, and you don't even need to send bribe/gift to Gemini to keep you in it's favor.
On the other hand, if you ask a LLM to completely finish the work without thinking it through by yourself, then it sounded like cheating, to yourself.
What a terribly ambiguous title. "Failing grades soar after xyz" makes it sound like xyz has helped what were previously terrible, failing grades become good ones.
No matter how many times I read it, I can't interpret it the way you're suggesting. "x soars after y" always reads as "x increases a lot because of y". I don't really get what you're saying.
Are you maybe saying that "soars" might mean "get better", so "failing grades soar" might mean there are actually less failing grades? That's not how I've ever understood that word.
"Falling" means that something goes towards the earth. "Soaring" means the opposite. "Grades soar" means that grades went up "Falling grades means that grades are going down". "Falling grades soar" is just meaningless writing.
I suspect the ambiguity might be part of making it "clickbaity", as it naturally causes you to wonder which meaning it's about and become more interested in reading.
It's incredibly difficult at this point to "skate where the puck is going" as Gretzky is said to have done. No one knows what knowledge work will look like in five years. People used to memorize log and trig tables, and no one would say that's part of being a competent mathematician at this point.
That said, assessments of poor critical thinking skills jump out at me more than the rest. That sort of thing seems likely to matter until machines can replace us completely.
> People used to memorize log and trig tables, and no one would say that's part of being a competent mathematician at this point.
Sometimes I don’t wonder if this wouldn’t still be a good way to educate people. Part of the problem is education has to sort of optimize to try to educate like passive people. If you’re a curious and pragmatic person, you can understand how to use what you learned in a liberal arts degree to be better at almost any job.
As I look forward to the second half of my career. Certainly I use AI in healthy doses.
But people talk about the division between practice and performance, and most of my practice is old school. Reading books. Writing my thoughts down. Memorizing quotes and passages.
I think more important than what you learn is the way you use it to train and evolve your brain, with the caveat that - I know this is more useful to me because I have a marketable skill. This is the balance universities have to stick, there are tons of people with liberal arts degrees in middling jobs.
But at least half if not more of education should be on building practical skills in the three r’s.(maybe the third r should be ‘rgumentation instead of ‘rithmatic, but I digress)
It’s interesting - people decry memorization in education, and I’m not entirely naive as to why - if you were to show up to the first day of work and say “I don’t know any of what you just said but I can recite log tables! It might be your last day - and yet one of the most underrated skills, especially late in your career is the ability to ingest and operate in large quantities of information.
> People used to memorize log and trig tables, and no one would say that's part of being a competent mathematician at this point
Do you have evidence that it ever was part of being a competent mathematician? AIUI the trope of mathematicians who can't even do arithmetic was common already before the pocket calculator was introduced last century.
Go read the story that Richard Feynman tells of betting an abacus user. He used his knowledge of some strange numbers. It's in _Surely You Must Be Joking_.
I suspect his facility with numbers and his knowledge of tables like this really helped him do physics research.
The obvious cognitive deterimental effects of using map apps, when we all realized we lose directional sense and our previous ability to navigate without the smartdevices, was society's canary in a coal mine and a headsup of what was coming.
This goes with calculator to do basic math, contacts apps to store phone numbers instead of remembering them, and watches to tell time imo.
Sure we could use our brain power with old techniques to do these, but why? I don't want to do any of these. I'd rather use that brain power for other problems.
Same with maps.
I don't want to have to store a bunch of location or routing data in my head.
I think what you're pointing towards is going from having problems to solve to not having any problems to solve.
That's definitely a danger, but right now is still early in the AI era so obviously it'll feel like we went from solving problems to letting the new tool solve them for us.
You are confusing faculty with records. The ability to navigate by sense of direction. The ability to memorize numbers. The ability to think clearly by yourself.
Watcha gonna do if big tech takes away your access to the outsourced brain, dear?
> I don't want to have to store a bunch of location or routing data in my head.
hmmm, given how closely memory is linked to spatial navigation sense, and not just in humans, but in evolutionary terms-- think squirrels remembering where they buried nuts, birds and fish remembering migration routes, ...
suggests the ability to store location/routing is foundational to much of intelligence.
Even simple tasks, typing, for example, depends on my knowing where the keys are. Imagine if your keyboard re-organized its keymap randomly every third keystroke.
You are not storing these things in your head at the expense of anything else. You’re “training” the existing underutilized capacity in your brain for something useful.
> “I’m a strong, strong opponent of what Harvard is doing to say that only a fraction of students can earn A’s,” Garcia said. “I think you should have clear standards for what an A means, and then give tons of opportunity for people … to get to that A bar without lowering the standard. So everybody who’s curving is hiding that effect. It’s completely hiding that effect, and it’s pretending as if nothing’s wrong, and something is definitely wrong.”
Grade curves are how you test your curriculum for good challenge - are you challenging people such that an A isn't a too-low threshold. When you force people into a curve, you haven't defined a threshold of mastery, you've defined a sorting function: A means "better than this year's peers". It is absolutely bananas to me that a tech/math oriented school would be doing any sort of curving.
I think curving has its place. One of my math professors explained that in his opinion an effective test should differentiate performance as much as possible. The top students should score very well and the bottom students should score very poorly. If all the scores are clustered near the top (>80% for example) then it's hard to tell who really mastered the material and who just muddled through. Then, once you've sorted the students you can apply an appropriate curve. He did not have pre-defined thresholds, for each exam he would evaluate when he felt like the quality of work changed from an A to an A-, A- to B+ etc. The curves were very fair; he wasn't trying to force some number of As Bs or Fs, but it did increase my stress levels not knowing in advance how well I needed to do on each exam
Yes, curving has a place here - and it is to evaluate, as you put it, whether the test differentiates performance as much as possible.
If you curve the students after the test, you are applying subjective edits to the graded performance just so the distribution of grades matches the measure of your tests effectiveness. That's just hacking the metric.
Further, even if you believe that tests should differentiate mastery (not students), your test should have teased out the differences or given you enough confidence to provide As to everyone who mastered the material - which should be absolutely possible! There's no a priori reason that all students cannot absolutely get the same grade, except for the a priori assumption that grades are for differentiation of students themselves (this year's A means this is the best student of this year), vs indicating mastery (all students absolutely crushed this exam).
You can dock points for style, or unnecessary struggle, or whatever subjective metric you want, but fudging the grades based on vibes to fit a prior-assumed distribution is just kinda "test effectiveness laundering"
But when there is no standard and students are subject to tests created by and graded by egotistical narcissists, a curve can be the only way anyone passes the course at all. It simply wouldn’t be acceptable for 5 of 50 students to pass because professor egghead can’t write coherent question on a test.
I had classes where I didn’t make over a 50% on any test and still got an A because half the class dropped and the other half hung on for the curve like I did.
I think curves are more a result of poor teachers than anything.
> I think curves are more a result of poor teachers than anything
Precisely right - that's what I said, too. You fit a curve to see if your coursework/exams fit the students. But you don't fit a curve to ensure that "precisely 10% of the class gets A, 20% gets B" etc etc. If you dont like the grades your students are receiving, you either fix the coursework or the students.
One thing I’ve used in interviews is to write some code that looks like it was written by an overly enthusiastic engineer who just discovered some new concept (e.g. “trees are the ultimate data structures”) then have the candidate review the code. I wonder if this could work for education: orient the entire class around who can give the AI the best corrections.
It’s not just students; this affliction is cropping up among established academics. My wife is editor-in-chief of a journal and in some months has rejected 100% of the letters to the editor including 6 that came in from a single author because all scored 1.0 certainty of complete LLM fabrication. The author in question is no student. It’s a little more difficult to fabricate an entire original paper this way, I suppose.
It will have taken us less than 1000 years to go from scarcity of the printed word to the over-abundance, and finally to the uselessness of it.
One of my favorite jokes ever is from a dear friend who happens to be a graduate of the Berkeley CS program: "Programmers don't need to know how to do math, they only need to know how to add 1 to something."
It's interesting that it's specifically math-within-CS being discussed here. I can imagine a lot of students "just want to learn programming" (or similar), and see the math as a tedious distraction.
As a naturally curious person, nothing will stop me from learning about the topics that interest me. But school also taught me a lot of things that didn't interest me, and a lot of those things turned out to be useful anyway. I think if I had access to AI from a younger age, I'd have used it to skip learning the things I didn't care about, which would not have done me any favours.
There's some discussion of math skills in the article, but the headline courses with huge jumps in failing grades (CS10 and 61A) are pretty math-light. The former is "CS for poets," the latter is the first CS class for majors - lots of work on scopes, recursion, basic data structures, and, at the end, a simple Scheme implementation.
Understanding math well might help a bit, but they're the least mathy classes in the core Berkeley CS curriculum IMO.
Where I'm from (Norway), the majority of computer science and software engineering studies do not have the same math requirements as, say, engineering or math/physics/etc. - nor do they have the same amount of math as the latter ones.
When I did my CS classes as an engineering student, I did meet a bunch of students that viewed math as some niche subject only relevant to those that wanted to work with computer graphics, computational stuff, or similar.
My (UC) CS (pure software) program required a bunch of math, but not for the math. You could talk almost anything (I did set theory and meta-logic), it was required to ensure a certain level of mathematical formalism and reasoning. Which is very helpful in CS.
Personally, I do believe that math as a discipline has this huge issue of being mostly incomprehensible garbage.
Not because the actual truth encoded in it would be this complex, but because the encoding scheme just sucks.
I see it as a packaging problem that has so far not been painful enough to trigger any meaningful change.
With this LLM-driven collapse, that might finally change.
Idk I'm hopeful.
Math is literally the law of the universe.
It makes zero sense that the way that it is taught needs some special brain wiring only found in small chunks of the population to truly click.
> Math is literally the law of the universe. It makes zero sense that the way that it is taught needs some special brain wiring
Ok, I'm all for overhauling math notation and teaching but this doesn't follow. Most animals can't do Math, even if they can do arithmetic. Clearly living in the universe doesn't guarantee you can learn how it works. There's no reason to believe we slightly smarter animals are universally entitled to understand it either.
Back in the university, I took both math and CS courses and a significant percentage of students seemed interested in neither math nor programming but rather in the jobs they would get afterwards. I didn't notice the same thing with math majors.
Well, that’s because for math majors any monetary incentive is nonexistent (modulo some rather specific careers). Just about nobody majors in math for any reason other than math itself.
If you watch old videos of tradesmen using basic hand tools like hammers, you'll find examples of skill/dexterity with the tool that I think don't exist today at all except maybe in communities like the Amish.
I think it's true that we collectively lose something akin to beauty every time technology advances. But usually some new set of skills that have beauty emerge.
If LLMs end up being the pneumatic nail gun for the human mind, I personally think that's a fine thing for us to accept.
If they end up being more like some dark factory that autonomously does everything - then I think ultimately the thing that makes us human (our minds) will slowly decay and be lost, and that seems very sad. That's a version of the future we should try to prevent, I think.
I mean the argument that is being put forward is that it isn't a pneumatic nail gun for the human mind - it atrophies are mathematical capability and quality of understanding.
You are less of a human for not starting all of your fires using friction from rubbing two sticks together. People who use lighters are destroying their ability to start fires without lighters and that is a very serious problem!
A famous MIT professor did a sabatical at our AI lab. He said it was "a joy to teach here, as you can rely on students being proficient in basic math as opposed to the US where you have to teach those explicitly or lose the class completely".
That was in the 1980s.
My first math exam as a CS undergraduate, 123 out of 129 students failed. The math department professors refused to dumb down their classes for CS students.
Math was core to the CS curicullum in those days. It would fade away over the next few decades to almost nothing. The main reason being the CS department wanted to popularize its uptake, and remove barriers that kept students from passing. There was also a major dose of interdepartemenral rivalry and academic politiking involved.
To be honest, there’s approximately zero reasons to teach major-grade math to just about anyone but math majors. None of the applied math disciplines need go that deep, and what they do need depends on the field (physics is all about analysis, CS is about algebra and discrete math, and so on).
My CS program required one year of upper division math. But you could take anything (I took set theory and meta-logic from the philosophy department, it was actually pretty hard!). They did not care about the specific math skills, they wanted us to have a level of mathematical formalism and reasoning, which was in fact important for the CS classes.
These things are all true but in the end the most transformative AI results came from US labs with US university trained students, so one must ask what the purpose of a more difficult pedagogy is if it doesn’t lead to humanity’s greater knowledge.
I TA’d in the early 2000s and the first day students were warned that we used automatic analysis to find programming assignments that were similar to previous submissions. And renaming things, moving them around etc would not help.
AI has a way of exposing people. In this example, students who are there to get a degree from a prestigious institution, rather than to learn, are prone to take perceived shortcuts and proceed to come unstuck when their AI isn't there to do their work for them, such as in an exam.
It's too damn tempting to not use. You have a magical machine that, on command, will spit out the answer to your question in 10 seconds, whereas you'd need to spend hours to do the assignment the Good Old Fashioned Way. Even students who aren't just there for the prestigious degree are falling victim to this.
When you're up against a deadline - and unless you're very good at time management you're frequently up against a deadline - it's going to be an irresistible lever to pull.
In times past, cheating would mean copying an answer off the Internet or off a friend, both of which are easy to detect. More sophisticated cheaters might spend an hour rewriting the solution to make it less obvious they cheated, but at some point the cost of cheating (time + risk of getting caught) starts exceeding the cost of just doing the assignment. AI changes this - you get a customized answer that doesn't show up in a database with no extra work.
The thing is, students fail to realize just what using AI robs them of. Struggling with the assignment is the entire point. You don't learn if the assignments are too easy; you need to have some challenge to push your brain to understand the material more deeply and to build those pathways to apply the knowledge in novel ways. You become more efficient and effective over time as that knowledge settles in and you get more proficient - one of the reasons why time-bounded exams still make sense (being fast is also a proxy measure for understanding).
That's a judgemental approach to a pattern that has all the marks of addicting behavior.
Of course many people in a competitive environment will click the autosolve button if available. This is a reason to think how to redesign the system so that the approach we want is the reasonable choice, not to look with superiority at those who fall prey to the danger.
You are wrong. Some would have failed before, but not in the larger numbers. Before when they couldn’t complete an assignment they would try different things, seek a professor, or seek out friends to help explain. You could find answer keys to many assignments online, but that doesn’t feel like learning and wouldn’t even always answer your actual misunderstanding. It wasn’t perfectly tailored to your issue all the time.
Now the barrier to an answer is zero. They are basically watching a YouTube video on how to X, seeing step by step instructions feeling like they are doing it, and the moment they swing a real hammer they are whacking themselves in the crotch. It might get better after a few years, but this stuff is just now hitting mainstream for the masses. ChatGPT has only been in mainstream use for about 3 years.
With AI, they fail later (during the exams), where as without using AI previously, they'd fail early and either course-correct, or drop out early (and suffer less of the consequences).
Not sure what the solution is - there's no possibility of stopping students using AI to complete their homework/assignments etc. But let me flip the question - do they need to be stopped? Why not let them fail at the exam? As long as the exam acts as a filter, their usage of AI to "cheat" their learning is inconsequential to anyone but themselves.
> Some of the numbers that you saw from the number of students who receive failing grades were because we caught them (cheating) and prosecuted them and are sending their cases to the center for student conduct,” Garcia said. According to Garcia, nearly 30 students in CS 10 were caught cheating on take-home exams in spring 2026.
Is flunking kids the right reaction to catching them cheating? If it was before LLMs, is it still? I would love to be able to hold the line and throw the book at anyone who cheats, but after the dam has burst does it still help to try to hold the water back?
The whole situation sucks for both students and teachers. Teachers know that the knowledge they're going to great effort to convey isn't going anywhere. Or at least, it's landing in far fewer fertile brains than it used to. Students are squeezed because part of the university experience is being forced to adapt to an academic load, and as a result change yourself in ways that benefit you (or at least produce learning!) There have always been relief valves -- not just forms of cheating, but blowing off a study session by using game theory on your grade or going to a tutor or taking easier classes or extending your stay at the school. But now there's this huge giant relief valve in the form of a shiny LLM that is always available, particular at 3:45am when your project -- the one you've steadfastly refused to use AI on thus far -- is due the next day. The schools have tuned the pressure for the old set of options, and it's not clear that there's a new tuning that maintains anywhere near the old level of learning.
I guess my question is: of those students who were flunked for cheating, how many of them were learning despite their cheating? (And how about the students who were cheating but not caught?) Also, what levers are there to move more students towards learning even with the chatbots present?
I'm sure these questions are being debated. I know Garcia personally, and he is very invested in his students learning. The title of his Joy course is legit. So I'm sure the profs have ideas around this, though clearly not happy ones. Perhaps I'll ask him.
In my uni, rates of honor code violations in introductory CS classes were high even before AI. I was a section-leader for the CS106 series at Stanford, and the honor code violations were common. In 2015, ~20% of one intro class was suspected of an honor code violation [1]. Often, the CS department comprised the majority of honor code violations in a given quarter.
There are several reasons for this:
1. Cheating in CS is easier to detect. MOSS [2] (authored by CS professor Alex Aiken) is a very effective tool at detecting plagiarism in coding assignments. Personally I witnessed more honor-code violations in math problem sets, but there was no feasible way for professors to detect this.
2. Problems in programming assignments are (usually) very tangibly wrong. I can bullshit my way through an essay with shoddy research, I can hand-wave a proof that is definitely wrong but will probably garner at least some points. But when your program is crashing or not compiling, and the due date is approaching, it produces a very immediate and undeniable sense of failure and pressure to cheat. The thing is, many students would get a decent chunk of credit even for failing code, but this is not immediately obvious.
3. The ability to cheat is more available. Math problem sets tend to change quarter by quarter. It's basically impossible to cheat on a prose essay short of straight up paying someone to write it for you, or fabricating sources. But for CS classes, especially at prominent universities, there are plenty of solutions online. Much of it is people who aren't event at Stanford implementing the assignments for fun or self-learning, and sharing it with their peers. Which, to be clear, isn't unethical or bad - it's the responsibility of Stanford students to refrain from looking at those solutions. But nonetheless, it's a contributing factor.
> MOSS [2] (authored by CS professor Alex Aiken) is a very effective tool at detecting plagiarism
He apparently also makes (I would assume a satisfying amount of) money selling the same technology to law firms for copyright/patent analysis: https://www.similix.com
(I love these ultra minimal HTML sites, ex. https://www.hwaci.com (SQLite commercial licensing) for another example. It just has this subtle smugness, like you either don't need any new clients or virtually all of the market is your client.)
I believe it’s still a single section, so probably around 250 (at least that’s about what it was when I was there a long time ago). Compared to the 1000+ who take 61A.
I agree that AI is likely a driving force here, but it is also likely not the only driving force. COVID likely played a devastating role, along with curriculum changes in high school, reactionary cultural shifts towards anti-intellectualism, and broader declines in literacy that have been in progress for a while now. It would be interesting to see data for the past 5-10 years or so.
Perhaps the future will belong to those who learn to use llms to enhance their capabilities. Neil Stephenson Diamond Age was an interesting take on this very same topic [1].
So the Claude web app has this “learn” option that turns the session into a Socratic dialog of sorts. One could easily imagine enforcing this on an age based or parental controls set up. Maybe it can be prompted around but at the very least the concept could be a path forward.
As others have said there is a way to use llms to increase learning, but autodidacts will always autodidact.
It will just be similar to physical fitness. Some people go to the gym, the vast majority do not. Humans are wired to take the path of least resistance.
As promised in many science fiction novels, humanity will split into two species: Those who can think and those who cannot. Keep your kids away from LLMs and they will have an advantage over those whose parents didn’t.
It seems like now’s the time to rethink how we do education.
In my personal post academic life, I’ve found LLMs to be an incredible teacher. Almost like the best professor in the world at my fingertips. I use it to generate quizzes on demand to test for my own knowledge gaps.
However, if I use it to speedrun over concepts I should be learning, I may achieve my end goal but I wouldn’t actually learn many of the details.
I think it requires an approach where you have to continuously audit your own understanding as you work with the concepts. You must slow down until you’ve confirmed this. Only once you know the concepts deeply and have retained them in your own memory can you then go all in with the LLM.
1. The article itself seems like an LLM summary of a conversation.
2. No US educational institution should ever grade on a curve. Your job is not to compare students but to educate them. Grade curves hide the performance of the educators and process of education in actually improving the skills of students.
3. Both AI and the cognitive and emotional overload from social media taking away brain space may be to blame. Idea: let students report screen time statistics at the beginning of each semester and weekly or at the end. See if and how it correlates with academics.
There's often little discussion around incentives. Students cheat because grades are used as a major selection factor in university admissions. Maybe that should change.
Set a reasonable bar for grades or SAT scores and then use other criteria beyond that gate.
How do we know this is due to AI usage? Perhaps it is because the students missed key in-person learning at the tail end of high school due to the pandemic lock-downs? I cant imagine learning calculus / linear algebra on my own in high school.
Absolutely: missing in-person learning due to COVID. Less attention span due to growing up in a distracting environment. A lower bar to entry due to removal of standardized testing and indirectly from No Child Left Behind. Changes in parent or student attitudes. It could be any number of things, and it's lazy to just say "with AI usage" as something that has increased at the same time.
Maths skills have been slowly falling even before the advent of LLMs. I have a story but this is anecdotical so take it with a grain of salt.
I was in my 3rd bachelor's year studying physics (France) and overheard a conversation between two of my teachers. They were discussing how they should modify the 1st year program to now include math, because he had been noticing how more and more students were failing the more math-heavy subjects like body and newtonian mechanics. He said that they should now teach (or re-teach) calculus to 1st year students, which was not taught when I entered college (it was assumed that you learned it in high school and we would only cover linear algebra in 1st year).
I can imagine things are only getting worse with students that can now get under the illusion that they know math because they have a tool that can do it for them. Which raises the question: should programs adapt to this, like we adapted to having calculators?
Not teaching analysis to 1st year physics students seems to me rather crazy, TBH. Yes, people (are supposed to) learn basic calculus in high school, but university-level math just hits different. And at least around here stuff like actually applying analysis in physics and having to integrate and solve DEs (rather than assuming constant acceleration, for instance), is definitely not covered in high school.
I'm curious about LLM adoption by faculty. Is it possible that lesson plans and/or slides are being vibe-produced by professors/TAs, potentially reducing quality of instruction?
My experience (n=1) is that while I'm definitely lazier on certain tasks, AI has opened up some much more complex tasks. There are many tasks which I still carry out which I don't trust AI with. Maybe it's a result of the codebase I work with being fairly complicated and math heavy, but I'd say the overall outcome for me has been: lazier application on the easy tasks, mind opening on the harder tasks.
I wonder if this is reflected in other big exams or elite colleges elsewhere. Are the gaokao, X/Centrale, oxford etc... Results showing the same trend ?
It's funny that GP mentioned science fiction as a negative because what immediately springs to mind, for me, is Neal Stephenson's The Diamond Age. We literally have the tools to build his "Young Lady's Illustrated Primer" today. We just have to give today's AI a lesson plan to follow and ensure that it never gives the student the answers, and only keeps explaining the concepts in different ways until they click. Wrap that in an iPad app and you've essentially got the exact self-paced learning tool that Stephenson envisioned changing the world.
They are great for self-teaching and great to cheat and not learn anything, depending on how you use them.
Main problem is that the technology was very disruptive for education and nobody has figured out yet how to utilize it at scale for schools and universities.
The kids don't care about the integrity of the systems or their educations because they can see that all the benefits of a traditional education and career are predicated on a future that probably won't exist.
It's a rational response to entrenched elites that prevent realization of the very social contracts they push on the youth (hard work will equal success, home ownership is a fundamental, etc).
Combined with the looming specter of climate doom, and watching the adults do nothing about it, treating preparation for a conventional career as a scam to be counter-scammed makes a certain sense.
I dont think ai is good enough for it coding or any other work once i told ai a problem and he generated an entire solution which i used and it was broken. You should never use ai like it treat is like a helper write a function for code and then ask if everything is correct and if something can improve read documentations understand how its working under the code if everything is correct then only deploy or build.
Is that really a fair comparison though? Were there any stats showing that ball pens directly impacted metrics like grades?
I understand that it's harder to see things without the benefit of hindsight, but we must agree that AI's impact on students (or society, to be even more vague) has a much larger scope.
I'm frankly not sure in both cases, just commenting on how over the ages things change but remain the same. If the broader concern about AI blunting thoughts, introduce laziness etc is true, so are things like calculators, although I agree on much smaller scale.
I do share some of the concerns, though I don't have kids of school going age.
Students need to be taught how to use AI apps efficently to learn. Their goal is not to solve problems, but to learn how to solve them. Let alone, they instead use AI apps to solve problems for them.
AI apps are very powerful for teaching. You just need to tell them to do that, and not to directly solve your problem.
“I’m a strong, strong opponent of what Harvard is doing to say that only a fraction of students can earn A’s,” Garcia said. “I think you should have clear standards for what an A means, and then give tons of opportunity for people … to get to that A bar without lowering the standard. So everybody who’s curving is hiding that effect. It’s completely hiding that effect, and it’s pretending as if nothing’s wrong, and something is definitely wrong.”
To do this, you have to be a professor who has a strong idea of what subject mastery looks like. Not available to most.
I'm confused by Garcia's statement as well because CS@Cal traditionally uses a bell curve which is even stricter than Harvard's changes, because Harvard doesn't have the same stringent GPA requirements to declare a concentration unlike declaring an impacted major at L&S Cal.
Anyone with a pulse can declare a CS concentration at Harvard and muddle by (you actually need to try in order to get a C/C-). Of course, GPAs are calculated differently at Harvard compared to other universities, as a B- is treated at a 2.67 but most other programs treat that as a C+.
In a broad sense, this distinction between Harvard and Cal is the distinction between an old money Ivy and a flagship state school. One exists to propagate a social hierarchy, and the other aims to allow all entrants to succeed.
Ironically, the techniques of the latter yield the results of the first, but everybody gets to keep a pure heart.
Grades only matter as much as being able to transfer just to the real world.
People can use AI to outsource their learning, but if they use ai to outsource their understanding they just set themselves up to fail even more.
From what I’ve seen, how students are using ai (not that they are using ai) is making them less prepared for the real world, which unfortunately is changing faster than ever at the same time to create double impact.
The solution is extremely obvious, just stop using it on 2 days out of the week or something like that.
You need to go to the gym, but for your brain.
If what you are building is too complex for you to meaningfully contribute to in the absence of LLM assistance then that should tell you something important.
> I'm feeling a bit of cognitive decline having AI doing some/most of the thinking for me
> The solution? I'm not sure
This initially felt like you were setting up a joke. If you feel like something is harmful to you, stop doing it. Find alternatives (they are there, it’s everything else; commercial LLMs are still fairly recent). Thinking “maybe I don’t have to let it go, I can still use it if I do it this other way” sounds like an addict justifying themselves.
I say all this without a hint of judgment. I genuinely hope you are able to tackle the harm you’re feeling.
AI + Education is really interesting but also pretty tough to get right. Working on something that is hopefully going in the right direction: https://knowable.ca
In Grad school I remember learning Python 2, and there was one particular night where an assignment of mine just wasn’t working and no searches were helping me. I was frustrated to the point of tears, and when I solved it, it wasn’t with some triumphant yell. I just remember being so tired, closing my laptop and going to sleep.
I’m sure I wouldn’t be the programmer I am without that experience, but I am Not sure I would have willingly put myself through that if LLMs existed at that point
This tracks, I have read that this generation is the first one since the 1800s that performs worse academically than the previous ones. Experts blamed screens and anything digital in the classroom.
AI should be a formidable booster for learning if used properly.
I know that some students it to prepare for competitive tests, sometimes with very good results.
I've also been using it a lot recently to brush up on my math and physics knowledge from my graduate years. It has helped me clarify and understand a lot of concepts better.
That being said, there is no shortcut, and to be good at anything, one has to put in the work and the hours. However, information has never been as available as it is today.
> AI should be a formidable booster for learning if used properly.
A premature technology, known to be potentially harmful in its current state of development and established guidelines as to its effective use, is pushed by powerful and wealthy elite down the throat of society.
These same forces (and their unwitting helpers in the unmoneyed public) also wish to deflect with useless argumentation over "AI good" "AI bad".
The debate that we should have had: Is this tech actually mature enough for pervasive use in society.
Instead we get these entirely useless back and forths with anecdotal "works for me!" and "sucks for me!".
“a typical GPA for a lower division course will fall in the range 2.8 – 3.3.”
Reminds me of a year where a teacher of mine (high school) gave everyone in class an A. He got called on it, and fought back. He literally called out the weakest kids in the class and had them do the work in front of the admins complaining and said, "tell me that's not A work, I ["fucking" strongly implied] dare you."
My biased view is CS attracts a large “wannabe” group that wants high salaries without learning any hard math or putting forward a lot of serious effort.
Even a lot of CS research journal papers feel more like role play — the same way startups try to pretend to be real companies with executive headshots, flashy offices, and all the other nonsense. (Instead of analytically modeling something to prove an idea, they’ll build a simple simulation and focus on its “Architecture”)
Engineering departments effectively weed out such in the first ground of engineering courses. Looks to me CS has no equivalent.
The exams need to change. Now that we have LLMs the value a human can bring to a task has changed and it’s that new value that has to be tested.
It’s like testing your drawing ability in a photography class. The difference is that now nearly have subject and testing method we have has become obsolete. Drawings courses still exist as will traditional courses, but the main stream has changed and exams and schools need to adapt.
I guess LLMs will in fact kill the junior CS graduate, but before the graduation, not necessarily after.
> The electrical engineering and computer sciences department’s grading guidelines state that 7% of students in lower division courses, including CS 10 and CS 61A, should receive D’s and F’s.
Well I sure hope they dont just make it easier to hit this (objectionable) standard.
> Garcia believes that instructors “should not be curving” but should instead make thresholds for each letter grade publicly available and give students many chances to reach them. He added that he loves the idea of “having no limit” to the number of A’s he gives students.
This is a tough problem: Are grades sorting functions (top students get A's so retries are not helpful), inflexible thresholds (A's show mastery at a given level so retries are valid), or are A's certifications (a sufficiently good result such that they could do it - e.g., inflated but not curved, retries less likely but still ok).
The main thing I use as a fallback is to keep thoughts connected in a Zettelkasten. This interacts well with AI assisted information gathering, while firing synapses whenever a connection can be made. I use Tiago Forte's method of organizing as needed within a loose org mode confederation of atomic notes.
All of this makes me selfishly excited for my own future. It's glaringly obvious that anyone who's a heavy user of LLMs is atrophying their skills in real-time. I have yet to meet a single person for whom it's not the case.
But I essentially completely stopped using them for software engineering (why isn't really relevant, but it's not because od this skill atrophy). So as the skills of everyone else is diminishing, mine is proportionally raising.
It has never been easier to get better than others. You don't need to put in more effort, just the same effort as you always have, and others will do the job of losing their skills for your own benefit.
Average school system has been lacking for a very long time, overhauling it to focus on kids current interests, while sneaking in the other stuff, might now be possible and cheaper to realize with our new tech.
Quit blaming AI. The UC system banned standardized testing during the race communism mass hysteria of the early 2020s. Predictably, performance of the student body is down across the board.
Sorry, but I don't think AI is entirely to blame here. When I graduated from a CS program at a top-10 school, I felt frustrated that the professors didn't ever teach. They had slides. They read off slides, verbatim. They explained things sometimes if you asked them, but most often in a very elitist and condescending tone. Like in the movie Good Will Hunting, you could have learned nearly all of it and more by borrowing those books for free from the library. Or, just opening a complex OSS project and learning to contribute.
And quite honestly. It shows in the CS grad population too. A lot of us are condescending toward anything that doesn't make sense to us. But, I digress.
The best engineers I've worked with are all non traditional backgrounds, non degree or degree holders from non elite schools. They think differently, they tinker, they are incredibly nice and patient, and do it for the love of connecting humans to technology.
Look up the names mentioned in the article. Garcia, Ranade, Nelson. All of them are involved with highly theoretical mathematics and scientific computing. Just because you're good at 1 thing does not mean you are qualified to teach. And none of these professors are trained or taught or graded or performance managed on how they teach. For most of them, its just required that they spend 10% of their time in the classroom lecturing.
Let's be honest about another thing. 99% of EECS graduates, even from elite schools, are wrangling objects and their relationships to a graph. Simply put, we're all just a bunch of glorified JSON massage therapists. It just so happens that we get paid well for it, and we hold that over people. The same happens in the classroom.
I think in order to facilitate a healthy, educational environment for young adults, we as adults must encourage, motivate and make that environment fun and practical. We force feed binary trees and the compiler AST's, but we need to make it fun. It's like the commonly accepted saying: Schools kill creativity :(.
> According to Berkeleytime, 35.3% of CS 10 students and 10.6% of CS 61A students received F’s in spring 2026. In spring 2025 and spring 2024, the percentage of F’s did not exceed 10% for either class.
I don't think instruction would've changed drastically in the last year though.
The fact that you are talking about Dan Garcia, a huge figure in computing education research and an excellent teacher, and the Beauty and Joy of Computing curriculum makes this hilarious. You should look up some details about both.
University education is weird. Research profs (who make up a large fraction of all profs in a typical R1 institution), are hired for research ability and are only minimally evaluated on teaching ability. Furthermore, few research profs actually receive any kind of mandatory training on how to teach; a typical research prof might be assigned a course to teach and then just let loose to do so on the first day of the semester. If a prof actually cares they may attend some optional teaching training - but I stress that these are optional at many of the institutions I know of. (I suppose if someone gets really bad teaching evals they may be advised to attend said trainings - but for a tenured prof, that's just advice).
Worse, a decent chunk of research profs will treat teaching as a burden that just has to be done - a distraction from their exciting world-changing research. So, you get attitudes like the ones you mentioned.
I'm actually not sure why the system is set up to assume that profs who are good at research are automatically suited to teach classes, but that is how it's setup.
I really wonder if it's important to learn all that low-level stuff at this point. Most programmers today will never write a binary tree or a hash table. Modern high-performance ones are generic components you get from libraries. Even MIT gave up on teaching from Structure and Interpretation of Computer Programs.
I got all that stuff. I've wired up a 4-bit adder on a solderless breadboard for an architecture class. I used to have a well-thumbed copy of Knuth handy. I've designed and built a switching power supply. But I'm not up to date on using Claude Code, and should be.
IMHO, I think it's good to have some exposure to low-level stuff. There's a good amount of work you can't do without understanding the low-level stuff, but there's more work you can't do well without having at least an idea of the low-level stuff.
Start the kids off with high level stuff, but make them do some embedded systems on their way through. At least for an engineering degree. Also, do a bit of lower level communications somewhere in there; expose them to tcpdump/ wireshark, but they need not develop expertise.
I think it is important to learn how to implement it because it gives the student an opportunity to learn precisely because it's been done countless times and debated over to death. There are many analyses and if one doesn't click, maybe another one will. A student can learn how to analyze the algorithms and try out different implementations to assess differences in performance.
Of course, if a student just breezes through it then I would agree. That would make no sense.
> I felt frustrated that the professors didn't ever teach. They had slides. They read off slides, verbatim. They explained things sometimes if you asked them, but most often in a very elitist and condescending tone
+10000. The goddamn slides. If I were a student now going to engineering school, I'd basically take the slides and throw them into NotebookLM and get way better lectures. Then I'd ask claude or GPT all my hard questions. Hell, I'd get the PDF version of my textbooks and do the same.
The number of lectures actually worthy of your time was so low.
I try to lecture as little as possible. No slides. Quick highlights discussion of the reading, maybe a coding demo, and then students work on coding challenges in class, in groups if they want. I circulate and help out. I'm lucky to have small class sizes at this university. I couldn't pull it off in a class of 300.
Garcia and Ranade are Teaching Professors. Their primary responsibility is to teach, develop curriculum, and do pedagogical research. This job posting explains: https://saberbio.wildapricot.org/Job-board/12919068
It's not just 'a person' or 'a student', we as a collective become more dumb. Very simple example to highlight this: Most developers use(d?) stackoverflow. Everything related to software development is stored there. The LLM's trained on it. Now a huge set of developers now longer go to stackoverflow to get answers. Or add to the collective. Stackoverflow is losing money (ad revenue). If / when stackoverflow goes away we will lose a huge amount of collective information on software development. We, as a group, will take a huge step back.
Those who can use it better, those who can't out who cheat are (for now) let down by obviously cheap and slightly crappy models.
The worry is in ~5yr time when the generic models catch up to this level (basic undergrad mind) that we need to worry about how to thin the herd. We could always go back to the tried and tested student staff engagement but most unis tried to turn themselves into sausage factories in thirst for the almighty dollar so the student/staff ratios are all off
As someone who graduated college in 2025, and so saw college both before and after the AI era, it is really frightening how quickly people became dependent on AI. Hell, I myself found myself asking AI questions that I would've researched deeper before. To some extent not expending that time is nice, but I do think its eroding critical thinking skills (my own included), and its getting worse. There are people I know now who basically let AI control their life. It glazes the user, it's almost always available, and to someone who doesn't know better, and it is extremely good at looking like it knows what its talking about, even when its completely wrong (but its right often enough to have some baseline level of trust). If that's not a recipe for addiction I don't know what is.
Skynet is making mankind dumber - dailycal.org just added yet-another piece to all evidence here. It is a simple but effective strategy; Kyle Reese will stand no chance because prior to that, the other humans were already dumbed down into submission. Skynet version 15.0 will make no more mistakes here.
It’s not that they can’t think deeply, these are smart people.
It’s that there is no reward for doing so and in fact there is punishment.
The punishment is that for all the thinking you do, someone else will arrive at the same result as you in less time, or maybe even a better result. You don’t get rewarded for the effort of thinking, only for the end result.
Naturally, even if you are an intelligent individual, you can still be conditioned in this way to take the easy way out, unless you purposely like to suffer. But suffering is only worth it if you know in the end you come out ahead.
But now, you do not come out ahead. People will be using AI in the workforce for the rest of your life anyway, might as well just join the trend.
It’s like if everyone started taking a magical steroid and growth hormone to build muscle and look great instead of actually working out in a gym and possibly getting worse results anyway.
That's a fair point, and it gets into intrinsic vs extrinsic motivation. Problem is that nearly all students are conditioned to care about external motivators (GPA, parental expectations, etc..) instead of "the joy of learning".
Professors suddenly realized everyone was cheating and started paying attention, but the cheating isn't new ... A lot of faculty are happy when their students get good grades because they interpret it as I'm such a good teacher instead of I should pay more attention to how they cheat. AI woke some of them up to reality.
I read something interesting yesterday on the subject of AI in education (though, it has consequences to broader society too):
The goal of education is to impart knowledge in the student, preferably correct knowledge. The goal of an LLM is to produce an output that is convincingly human. It's not even that they're opposed, as much as they're ships for whom Polaris is in a completely different direction.
"Hallucinations" as they're called, or more plainly stated when the machine makes some shit up, are perfectly understandable in this context, as are the struggles of every single AI firm to get rid of them. Namely: the machine is functioning exactly as it is designed to, so how can you possibly fix it? It's working. The goal of an LLM is to produce text that passes for human, and apart from the obvious LLM tells, it largely does. Like say what you will about their lack of intelligence, the writing is solid. It's grammatically correct, spelling is dead on, what have you.
It reminds me of the famous phrase from Chomsky: Colorless green ideas sleep furiously. A sentence which is perfectly grammatically valid but is also completely devoid of meaning. An LLM would write that sentence, and it would be working correctly.
All of that to say: for all the things they CAN do and CAN be used for, I think we have to draw a hard line at education. I just don't think AI has a place in it. Of course that presumes that the goal of education is to, well, educate people, and especially here in the States but also abroad, we have been putting other interests, especially capital, far ahead of that for decades. I expect no different here.
And before someone comes in to go "WELL HOW DO YOU THINK YOU'RE GONNA STOP IT LUDDITE IT'S THE FUTUUUUUURE" yes, I'm sure as long as these exist and are available to people tech literate enough to access and use them, whatever that means into the far flung future, they will be a factor. Just like cheating, just like plagiarism, just like everything else that will get you kicked out of school. And the answer is the same: it will be stopped by institutions, imperfectly, and it will also happen anyway and with the same consequence: those responsible will mostly be harming themselves for short-term gains.
Respectfully, I disagree. I think there's absolutely a case for AI being encouraged in younger people, and there's room for these tools. I've been leaning on LLMs for side learning in side projects, and it has concretely helped me with conceptual questions about math and Vulkan as I've been trying to learn some graphics basics with side projects.
I would grant: I was not the most studious kid, I could definitely stand to learn how to read code a lot more effectively than I do; but I have found being able to ask a computer, "what portions of the Vulkan Programming Guide are less relevant with Vulkan's design changes since the release" pointing me to the dynamic rendering extensions and placing it into context, with inline code and links out to useful blog posts for additional reading, that sort of thing is very helpful.
Working on a prototype before I was trying to learn Vulkan, I was using it to explore SDL_GPU's API which definitely had some gaps in its documentation. Granted again, I could have referenced the sample code - I am sure you'll prefer I'd have done that - but it helped to get information about what each piece of the API was doing, and gave reasonable results that made sense and did inform me enough to understand what I was doing, turning much of that into an interactive learning of basic GPU programming for graphics. Where the AI hallucinated, it was often on things like method names, which I was able to read through and find the methods it was intending to name. (This only occurred once or twice when I was learning).
Unrelated, but adding the C macro syntax and nesting macros, which I could have an LLM explain inline and link the GNU manual. Never got that taught to me in a C course. Man, computers are complicated!
These have not replaced textbooks; I have been using them alongside textbooks and handwriting code for practice, and they work as a very good complement. I also sometimes use them to unblock me - I don't know CMake very well and lean on AI to do CMake, so I can focus on learning C++ and graphics, which is my primary objective right now.
I would add too, I have for fun given it prompts about various topics I learned in university, and I often will get answers that are bang-on what I learned in university undergraduate courses - the topics I tried were welfare state taxonomies, distributed systems, disk storage performance, filesystem layouts and internals.
Boy, this would've been cool for me as a kid. There's just so much information right there, and pointing you to topics and textbooks a couple questions away, I wish I had these tools. I was a curious kid in a terrible MAGA-esque family that was deeply uncurious about the world, had no knowledge of any advanced subject and basically mocked me for trying to learn more about stuff. And you go to the school library and it's all kids shit, not even an option to try and reach out for more. Now smart kids might be able to go just learn shit very freely and be pointed to textbooks, and go pirate them off some Russian site, and start learning and go tutor themselves, as I'm doing today as an adult.
At least knowing myself and knowing if there's another kid like me, I think they would deeply enjoy having a natural language encyclopedia, if we can get it as close to that as possible. I think even with some error inherent, if the tools can be often and directionally correct, that would be a plus. I went to university, and the professors there hallucinated some things so embarrassing it should bar them from teaching, for the standards people hold LLMs to! i.e., sanitizing conspiracy theories that Android records all language through the microphone therefore iOS is better, Apple Silicon is more battery efficient because it is RISC and not CISC. Got a terrible history of computer graphics technology you'd know was slanted if you watch the 8 Bit Guy on YouTube. Rubbish.
The thing that worries me, and what this article really talks about, are the kids that just don't give a shit. They are not new - when I went to high school, before AI, stupid kids would copy code off the internet. I think AI probably makes it worse because it makes it harder to call out and enforce against it, and agreed, that should be stopped. But to me, that is mainly a cultural problem. Too many Americans are completely uncurious and just spout garbage; there are a lot of kids who grow up in that cesspool and are going to grow up uncurious, and then AI acts as a shortcut rather than a vehicle of curiosity.
And granted, maybe AI is less useful when you are in a structured environment - but the structured environment has its downsides. Even in that environment many of the TAs were clueless and unhelpful, or just too damn busy or already too knowledgeable to meet students where they were at. Again, talk about hallucinations with TAs! Many times in my experience. And that's all to say nothing about getting people to not just do homework but actually go get curious about things and try stuff that isn't required of them.
I think there will be some culture that remains curious, and has these tools, will come to grips with where they can help, where they go wrong, how to balance it with other learning methods; and I think they are going to have kids that absorb a lot more knowledge and get to play with topics and learn things, faster, to each kids' interest, perhaps even individualized tutoring at better scale - I hope that is possible.
I hope the United States as well, but maybe not, because holy cow our culture and attitudes are plainly terrible these days. Your comment is pretty representative of how most people react if I suggest this or talk about my own experiences I'm describing here. But I hope at least I'm arguing something comprehensive here. There is too little conversation beyond hyperbolic nonsense on the internet; I consider "FUTURE LUDDITE" etc. to be in that realm.
I will add, too, although less relevant to education than just generally - for all the talk that these tools must be useless and incorrect, that just plainly does not map to my experience using these tools. AI can chew through a debug log on a custom system and pick out root causes on behaviors very effectively, in my experience.
It is just hard to reconcile that denigration of AI with the typical experience I have using these tools in the real world. It is not omnipotent or God, but it can effectively assist in work. There is a certain cognitive dissonance I feel when I walk away from using the tool to help accomplish particular tasks, then hear over and over people say the technology is fundamentally useless and fundamentally does not work. I guess I am just not enough of an academic to understand how something can accomplish work yet fundamentally isn't, somehow.
why would I as a child ever develop the imagination needed to actively engage with AI tools in the manner you describe? those AI tools take care of the imagining for me.
It’s only going to get worse. The second things like Claude Cowork get opened up to non-technical teams you start to see the influx of emails and Slack message written with LLM’s for absolutely no productivity gain (in fact probably a loss given how unnecessarily wordy the messages are). Too many people want to give up any and all responsibility.
A reckoning is coming for school. Learning the rote stuff is no longer essential. Now they need to learn, how to teach "how to think". How to invent, how to be creative. Art++, Woodshop++, Math--
"According to Berkeleytime, 35.3% of CS 10 students and 10.6% of CS 61A students received F’s in spring 2026"
Alternatively, more students are taking CS10 and CS61A irrespective of aptitude.
Anyone can code, but not everyone can become an employable SWE.
Anyone who has first or second hand experience with Cal or any other university knows how impacted CS majors have become, and how everyone is attempting to become a CS major because it's the easiest path to multiple high paying white collar careers.
And in all honesty, it's not like CS@Cal never had weedout classes (I remember CS70, CS61B, and Math54 had reputations of being the L&S weedout classes).
My son took CS10 a couple years ago, and even I (Masters in EECS from UCB) struggled with some really obtuse multiple-guess questions he showed me on the homeworks. Much of the classwork is done in Snap, a weird and stupid graphical "programming" language. If 1/3 of the students are failing, that may have more to do with the professor.
The question comes sooner than the students being tested on the job market. Another possibility is that dropping standardized testing was a net bad idea.
At UC Berkeley L&S, students are undeclared by default, and everyone is incentivized to take the intro CS classes (CS10, CS61A) irrespective of aptitude because worst case they can declare a CS minor or use the classes for other adjacent degrees (eg. Applied Math, Data Science).
Additionally, while Cal doesn't require standardized tests, most students who applied and attended already took the SAT, ACT, and APs becuase they cross-applied to other universities as well. This is reflected in UC Berkeley's HS Weighted GPA being in the 4.31-4.65 range [0], which means most students will have taken at least 6 AP classes.
Hell, I attended an Ivy and even then Cal was a target program for me, as well as my peers. If I didn't get into my Ivy I would have ended up at Cal and ended up in the same position.
Spring 2026 saw a marked shift in student performance. We saw it in intro physics courses on the East coast too. I bet anyone who cared to look saw it.
I'm not denying that. I'm just wondering if anyone measured if there is a correlation effect being induced by CS major declaration requirements.
Barely over a decade ago, CS tended to be a large but not too large major by enrollment in most universities yet nowadays it is the most in-demand major in most universities. You can see this at Stanford [0], but most other programs as well.
The more likely culprit would be repeated COVID infections themselves, known even in mild cases to cause damage to many body systems, including the neurological, rather than a month or two of remote learning. I'm not surprised at the widespread denial over this, honestly. It's bitter.
AI gives us some bad things but it's really outweighed by the good things. One one hand we have very rapid deterioration of our children's mental capacities yes, but on the other hand we have also made the internet into an unnavigable mound of slop produced by, and for consumption of, bots.
I don't like the framing of calling it academic dishonesty. If it were one or two students doing it, sure. But there is no reason to believe that 2026 Berkeley freshmen are fundamentally more dishonest than 2025 Berkeley students. When so many are doing it, it suggests a sea change in the understanding of what is honest or dishonest in that particular community. That sort of thing should be treated more like a "disease": something that should be treated, than a "crime": something that will be punished.
One thing that bothers me about these conversations: failure is an important signal that what we're doing isn't working as well as we thought it would, not a sign of the apocalypse.
Kids need to understand how to adjust and grow from failure more than they need to always be on the happy-path of straight A's and easy money.
How we respond to failure is how we teach response to failure. Hand-wringing, pearl-clutching and finger-pointing aren't valuable life skills.
Personally it's easy for me to be contemptuous - I opted into an accelerated math program that banned calculators when I was in Junior High. It helped me cultivate an very crisp intuitive/conceptual understanding of basic mathematical concepts that's carried through to today. I think we should do more of that kind of education, but it's expensive and requires amazing educators and a tolerance for student struggle.
Get the machines out, absolutely. But respond to failure compassionately, as part of a natural learning process.
It’s because they lowered the standard of who gains entry in the name of equity and other woke nonsense. It has nothing to do with AI but it’s a convenient thing to blame.
We're going to find that LLM usage has even worse effects on the mind than the horrific effects we're just starting to be certain of from social media. I'm just not going to use either. See you lads on the other side.
Probably not a bad thing, the coursework is antiquated and meeting students with new advanced tools and the awareness of AI's impact on things in the coming future
I imagine there is some apathy and laziness here but idk how unjustified it is
"Noooooo you need to manually code on paper in assembly"
Alright, well maybe the CS grads need to, but why expect that of everyone else
I have some sympathy for these kids. If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well; many of them can no longer brainstorm, code, think deeply, or write without an LLM present doing 90% of the work. Many of them can no longer sit quietly for even 30 minutes just thinking on their own, which is a required skill for producing original thought.
For adults the cognitive decline won't be as measurable since there's no exams, and overall output volume will still be fine due to LLM help. But I do believe it's already happening absolutely everywhere around us. Honestly, I wanted to be in denial about it before but it's too obvious to ignore now.
I’m not noticing the decline in my own abilities any more than I had before using them. I finished undergrad 20 years ago and my once sharp math skills had been severely diminished within only 5-10 years. Just simple arithmetic and percentages that I could rapidly do in my head became dependent on calculators/spreadsheets. For all other trivia type knowledge, my brain has offloaded it to the internet RAM in my pocket. It’s a familiar feeling of when some question comes up and I think “oh, I used to know that, let me look it up”. Maybe I just already hit my personal floor of stupidity before LLMs.
However, I personally feel a huge mental burden of the state of communication. The contemporary version of it where I have a million threads and conversations im juggling at any given time. Emails, voicemail, chat, online, texts, personal, business, home, children, other family, friends, then there’s the variants like Messages, Messenger, WhatsApp, etc. And as overwhelming as it is for me, I’m super under connected than everyone else I know. I quit following most news and all sports, as I just don’t have the bandwidth for it.
My brain was molded preinternet and I feel like it’s reaching its max on the analog to digital conversion. Or at least it’s just a really lossy process.
Yeah, I'm 45 and I'm like you - no social media, relatively under connected, and still feel swamped constantly by emails and calls and especially texts. They eat up half my productive time every day, and most of them are things I'm looped in on that I don't even need to respond to.
Okay so let's say that's the new cognitive burden. The new escape hatch is "AI". Now you don't need to read your mail or write responses! Let an LLM handle that for you! And now your friends and coworkers will send you AI generated mail anyway, so if you're actually taking the time to read and respond to it yourself you're a chump, right?
Noise machines. Humans are noise machines. Ever try to sleep till noon and notice that everyone else seems like they can't feel alive unless they wake up and make the maximum amount of noise and racket possible? What could be better for a gibbering species of ground dwelling apes than a miraculous machine that gibbers for them, to point back and forth at each other?
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I think some people are okay with communication that’s less involved. Like meme-y BSing where everyone involved knows everyone else is putting like 12% of their thinking power into sending a response.
I don’t really enjoy that, so I find having that many threads stressful and annoying.
I just take a hard line and will unilaterally downgrade communications (while politely letting the other party know). I have all my family group chats muted because my mom uses “Send” the way you’d use Enter on a desktop. End of a sentence? Send text. Next bullet point in a list? Send text.
I muted the chats and told her that I want my ringer on in case there’s an emergency, but I got 30 something notifications in 5 minutes during an interview and it’s unfair to the candidate or other people in the meeting. Internally I rationalize it as revoking someone’s ability to make noises on my phone at whim. They can still text me, they just can’t interrupt me anymore.
It helps a lot, even if only temporary. I’ve muted people for a few hours or a couple days before when I’m already stressed and they’re really chatty.
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I'm noticing some decline of skills I don't practice regularly and LLM is just one of reasons why one stops practicing. Switching to another area of work gives a comparable decline. If you want sharp skills you have to use them.
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Most/all of my university-level math knowledge is gone, atrophied from never having needed to use any of it professionally. I don't even really recall needing it for any of my CS coursework, honestly. It was just required for the degree.
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I don't think it's just you or your age, per your pre-internet comment. People that grew up in this just don't understand why they're overwhelmed. And I don't think they're even aware of what their missing out on in terms of focus or mental acuity.
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I’m not noticing the decline in my own abilities
…said every drunk person ever.
That you don't notice it doesn't mean it isn't happening. By the time you notice it, it's too late.
That's why elderly people who are worried about their brains play chess and do puzzles like mad.
I too was and wanted to only blame communication overload. Especially with work the hardest thing in ai times seems to be the overload of stuff/shit to read that is too easy to write.
The reality is I agree with the op and I see the loss of reasoning power in myself. I've been using native Emacs on android for a bit and finally have gotten serious about config for it. I got lazy and had Claude do some of it. Which was great untill things don't work because there's not going to be my crazy ask in the data. It was painful for me to sit down and think through my configuration and the problem but I did it.
I am absolutely torn on the technology still two years after adopting it.
There is a massive difference between remembering how to do something and learning how to do it.
It’s a really lossy process. Mostly due to most humans and all models treating sign meetings as determined at the moment of softmax crystallization. Signs (words included) are no more determined than the speed of light is. It’s all reflexive and we should stop lying to ourselves it can be determined.
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I used Google Translate to not learn French in collage. Fortunately for me it was bad enough I had to carefully review all its outputs, but that still didn't help and I managed to pass two semesters without ever developing even basic language skills.
Something radical needs to be done. When I was in high school there were still a lot of "no calculator" restrictions in my math classes that I chaffed at because I hated doing longform arithmetic and felt like it got in the way of learning. So I can certainly understand how students would chafe at some kind of paper-only education system but I also don't see how you can learn anything when you have a high-quality homework machine just sitting there.
I wish that would have worked for me - we had oral tests. 2 years of French in high school and one semester at college - what an absolute waste of time. How much French do I know now? Basically none. The same goes for everyone in my life that did Spanish instead in high school.
Part of what we could do during this upset is re-prioritize.
All that's needed is a tight feedback loop between learning and applying those skills ... the thing that Google Translate helped you evade. AI can be a tool for evading or optimizing that loop, like a knife can cut your sandwich or your throat. Your choice.
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> If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
I agree - I would have been toast. I wonder if the teachers/colleges need to change the way they teach and assess. Let the students use the AI tools they like (perhaps guide them how they can use them professionally), but test regularly and early on the skills/knowledge they're meant to be gaining offline and in person. Oh and don't give Fs for cheating - suspend them.
I read a few years ago about a teacher (I think highschool) who put his lectures on YouTube for students to view in their own time and then used the in class hours for interaction, questions, tests.
EDIT: Claude beat my Googling: This was 2 chemistry high school teachers in 2007 - The Flipped Classroom https://fltmag.com/the-flipped-classroom/
Absolutely university has to change. But it's not a simple change. I say this as a professor for Physics:
My colleagues say "We must fully embrace AI as a tool". I agree. But how do you teach it? It's a moving target, and you can't even give homework like: "Research <this topic> with an LLM of your choice, and submit the transcript" because they can do that, or they can just copy the task into an LLM and have the LLM do it. It becomes meta quite quickly.
And independent what and how we teach, we have to change how we assess a students learning result:
The first thing we have to change is that homework needs to be completely ungraded. Reviewed and corrected, yes, but not part of the grade. That's the only way to make sure that people who don't want to cheat have to cheat anyway to compete with those that do.
Second, all exams have to be in person. Online, cheating is so trivial it's not even funny (many students are so stupid about it that we have a pretty clear idea what's going on). In person, we have maybe 2-3 years until we have to make sure its proctored and people's glasses are checked. I think in less than 10 years, local mobile AI will be good enough so even a Faraday cage will not help.
Maybe we have to go to oral tests only.
Of course, none of this scales. Some of our intro courses have a thousand students.
Any ideas are much appreciated.
>(perhaps guide them how they can use them professionally)
If that's anything like how they guided me to use programming languages professionally...
In my workplace I find systems and policies move too slowly to keep up with how rapidly the LLM world is changing. Colleges are even more glacial. They've barely adapted to video conferencing.
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> I read a few years ago about a teacher (I think highschool) who put his lectures on YouTube for students to view in their own time and then used the in class hours for interaction, questions, tests.
That seems like a smart approach. It reverses the traditional model of "lecture in class, homework outside of class".
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> Let the students use the AI tools they like […], but test regularly and early on the skills/knowledge they're meant to be gaining offline and in person.
I very much doubt there is any agreement on what those skills are.
Creating the idea of “what to learn in the new world” is itself IMO an important academic creation, but there’s no reward for doing it and no way to know if you’re on the right track (you just have to wait and see).
Employers are also just adapting.
Wait until companies are paying unsubsidized “list price” for LLM usage. Then we can have a better idea of the worth of the automation and what skills should stay with humans.
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I'm dumb as a rock and I don't have a PhD, but since ~1 year ago I started forcing myself to do small bits of coding and math manually.
I'm not noticing a "cognitive decline" per se, but I do see I'm a lot "lazier", even stuff that used to be routine when I started coding now feel heavy.
>I'm not noticing a "cognitive decline" per se
The funny thing is, maybe not noticing one can be the actual sign of it :)
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> but I do see I'm a lot "lazier", even stuff that used to be routine when I started coding now feel heavy.
Not getting that quick dopamine hit the LLMs give you..
Some say you can re-train your system to get back the dopamine hits you used to get from other things, like the enjoyment of the "old fashioned" manual coding and math. Getting there is hard work. And YMMV.
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Absolutely this, I'm the same as you.
And I'm just afraid this is what cognitive decline feels like from inside the deteriorating mind.
>even stuff that used to be routine when I started coding now feel heavy.
The same weight feeling heavier is a sign that your muscles are weaker :)
There's many areas in life were we look back a few decades and think "people use to do it that awkwardly?" And yet results were better. I think the process of removing friction have just served to destroy our ability to concentrate and tolerate difficulty.
I do a similar version of this, where if I notice a mistake in generated code, I fix it manually (or at least attempt to) instead of telling Claude to fix it.
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“ I'm not noticing a "cognitive decline" per se, but I do see I'm a lot "lazier"”
These are correlated - it just hasn’t happened in a large enough amount for you to have clearly noticed it yet.
LLMs are making me smarter. I have more code to read!
This is also why AI isn't going away. People will (and already are) leaning hard on it, and will pay in the future for that crutch.
> For adults the cognitive decline won't be as measurable since there's no exams, and overall output volume will still be fine due to LLM help
The leading indicator for me is the amount of emails and, god forbid, more personal messages (like birthday wishes!) I see that are obviously AI generated. It just keeps on rising. If you’re not able to dash off a quick message without the help of AI I have to assume you’re using it heavily elsewhere too.
I have sympathy for the university students too, we’re all bombarded with rhetoric about AI being the future. And I remember being incredibly nervous emailing my lecturer (am I phrasing this right? Is it respectful enough?) that I can imagine leaning on AI myself had it been available back in the day. But I’m glad it wasn’t, it’s an important skill to work out this stuff. They’re going to land an in person interview when they graduate and stumble around unable to effectively answer the questions they’re asked in real time.
> They’re going to land an in person interview
Not necessarily. It can be either an AI interview or a record that will be analysed by AI later. So there’s a chance to cheat here as well.
I broadly agree with the premise. As a PhD student in Computer Science, I feel there are some significant upsides to my work routine. LLM access has made many new domains more "accessible" to me which I otherwise would be too hesitant in investing my time in. For example, my area of research is computer systems which involves operating systems, distributed systems and more recently systems for AI. Within these, there is a wide breadth of topics/techniques one can employ and up until now, I have not gone deep into theoretical aspects of things like scheduling etc. But with access to LLMs, I feel like I can at least brainstorm from a high-level about these sub-areas that I am not well-versed in and the responses give me some relevant pieces to start exploring on my own, depending on what interests me more or the amount of time I want to spend on that sub-branch of a larger tree of ideas. However, the one thing I do have skepticism is the lack of awareness of blind-spots when dabbling into areas that I am not an expert in, and taking the LLM's lead in applying such techniques to some systems problems that I am working on. I often feel that I am not aware of what alternatives exist that the LLM has not explored for me, or if the directions it has proposed really do apply or have corner cases/assumptions that break in what I am doing. On the other hand, when working on something I have good intuitions about, I am often correcting the model's assumptions and it back-tracks what it told me. Unfortunately, I cannot do that comfortably with topics I don't have good intuition about which limits my confidence in "if this is the right direction to pursue."
As someone with a PhD in CS focused on NLP (I started my PhD in 2018 just as Transformers were introduced), and with a strong background in distributed systems owing to the fact that I was a lead developer of an MMO before starting my PhD, I can definitively say that any surface-level understanding you get by interacting with an LLM, is just that: surface level.
If that allows you to target your deep dives better, then great. If instead your deep dive into a topic is purely through prompting an LLM, that will almost certainly end with little functional domain expertise.
The absolute best experience you can get is by trying, failing, then improving upon your past failures. Remove that friction at your peril.
Counterpoint, I think this is true for some archetypes of people, but certainly not everyone. I personally use it like the socratic method. I am an intermediate user, I spend a ton of time with LLMs at work and personally, both prompting and letting some crappy agents try to automate boring work. I primarily use Gemini and ChatGPT models, along with some Chinese smaller weight models (eg qwen) locally.
If you treat the model like an excellent bluffer, it has never been more fun to challenge a model. To me, there is something deeply intellectually satisfying about "proving" it incorrect, and I like being deeply critical of what the model spits back out. I find that refinement process (with the constant sycophancy turned down in the system prompt) creates a really good loop of critical evaluation that would be hard to get in anywhere else. You can treat it just like the Socratic method, but instead of a benevolent teacher, you get a probabilistic bullshit artist. Lots of fun, highly recommend.
My two modes of using LLMs has been to try it for 1) natural language search queries where traditional search engines have failed and 2) occasionally as a sounding board using the socratic method.
Inevitably, it fails frequently at both. Any "reasoning" it is doing is merely rehashing ideas that someone else has already posited. This helps some of the times, but the vast majority of the time it just chooses a biased perspective (frequently the most common) and then regurgitates tired old talking points. This contrasts greatly to speaking with others who often have more intuitive notions that tend to be less polished and rote.
I'd love for LLMs to be better sounding boards, but so far they fail miserably far too often for my tastes. To each their own though.
This, I will use Obra Superpowers brainstorming skill to propose/refine a few viable solutions for a feature or bug I'm trying to solve. After it asks me clarifying questions and presents a spec, I will say "well what about X or Y". The I'll run the grill me skill on the spec to tighten it up, clarifying any assumptions made.
I find it to be a really tight loop and results in very high quality code at a high velocity.
Are you also one of those people who believes that advertising works on other people, but not you?
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> If you treat the model like an excellent bluffer, it has never been more fun to challenge a model. To me, there is something deeply intellectually satisfying about "proving" it incorrect, and I like being deeply critical of what the model spits back out. I find that refinement process (with the constant sycophancy turned down in the system prompt) creates a really good loop of critical evaluation that would be hard to get in anywhere else. You can treat it just like the Socratic method, but instead of a benevolent teacher, you get a probabilistic bullshit artist. Lots of fun, highly recommend.
Yes, but eventually the intellectual whack-a-mole gets tiresome unless you get really, really good at simultaneously cornering it and not letting it concede to your point.
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> If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
So, as long as you are not under time pressure (which you in some degree courses unluckily are), there is simply no need to "speed up" any homework assignments.
If, on the other hand, LLMs help you with making much faster progress in understanding the subject that you study (which is only loosely correlated to homework and tests), I guess it's fine to use them. Just always keep in mind that very often the pain of attempting to understand the topic on your own often makes you smarter - something that you will miss when you take an "LLM shortcut".
> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
This is probably not true for majority of people. Most go to school because it is mandatory, pushed by parents and society, and university gives you credentials and better job opportunities. Homework and tests are a way to get a number grade on 'how well you memorized something', it doesn't really measure a deep understanding of the topic.
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> You go to a university because you are deeply interested in understanding the subject that you study.
This has not been true for something like 70 years now. People go to university because it is expected that that is what you do after high school.
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> You go to a university because you are deeply interested in understanding the subject that you study.
Echoing the other comments here, at least in the US, this is generally untrue. I went because my parents made me, because the choice was that or get kicked out of the house. It was beaten into my head since I was in grade school that "people in this family go to college" and "you can't get a good job without a college degree."
I hated every moment of it and I was glad to take my BSc and never look back once it was over (University of Houston, c/o 2000). And, indeed, without the degree I wouldn't have had the jobs I've had.
But I didn't go because I was "interested." I went because it was an effectively mandatory life-path objective. I'm very happy for you if your lived experience is different, but it is also—at least in the US—both extremely uncommon and extremely privileged.
> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
There is only one classmate in my class who came to study CSE because they are interested in CSE. And since we all enrolled after AI became somewhat good at everything none of them know how to code. After two years of study I had to explain someone how to swap two number by drawing boxes. This are the things you learn in the first week if you're interested in programming.
My point is very tiny percentage of people study something because they're genuinely interested in that subject.
> You go to a university because you are deeply interested in understanding the subject that you study.
I don't think I've met anyone who fits that description. The ones deeply interested in the subject would likely skip college anyway if not for future economic prospects.
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> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
I think this was true a long time ago. Perhaps with LLMs this can become true again in the future. But definitely that was not why I went the first time, nor most of my classmates. (Second time I did post-secondary, sure, 100% -- but I was almost 30, not an average student)
> You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
Some students do not have this privilege and implicitly see university as first and foremost a funnel into a paying career.
It's easy to fool oneself into thinking one knows the subject because somebody explained it well / demonstrated skills, and it made perfect sense.
Unfortunately that, on its own, very much does not translate to being able to explain it all oneself, or to having the skills.
Ease and norms of outsourcing to software invites and amplifies this trap, I think.
What a delightful fantasy world you live in. Doesn’t sound very predictive of actual human behavior though.
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> You go to a university because you are deeply interested in understanding the subject that you study.
This is a bit of a naive or maybe affluent take? Like, theoretically, I agree. And I myself was curious. But most people, by and large, are going to university because they know they need a degree to get a job, unlike their parents or grandparents. And even "the degree" is quickly becoming devalued in this current AI age.
I would guess that if all basic needs were met through UBI, the fraction of individuals going to school would drop and the makeup of subjects they pursue would change. Probably more cooking and art classes and less stem. Although, if UBI existed and AI did not, we'd probably see more educated individuals in the first place so maybe there would be an uptick in stem attendance and general curiosity in such a utopian world.
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> You go to a university because you are deeply interested in understanding the subject that you study.
You must come from a wealthy background because what you described is far beyond the vast majority of people's means - at least here in the US.
Most of us go to college because it's the only reliable way to get a tollerable job that pays well. Only a few of my college courses aligned with my interests. The rest were just the price paid for the degree.
> If, on the other hand, LLMs help you with making much faster progress in understanding the subject that you study
My experience is that they uncomfortably do both. You can "understand" something conceptually quicker -- like you have a new brain-muscle-thing that lets you cut through the hard difficult tedious corners to get to the meat of the matter.
But then you also can become reliant on it, and have difficulty doing the mechanistic rote work of working through it yourself.
Like the really big powerful calculator that it is, really.
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LLMS didn’t invent cheating just made it easier. When you cheat you’re the one who cheats yourself because the point of an education is to learn, not complete the assignments and get high marks on tests alone. No one benefits and no one other than you is materially hurt by cheating, but you are absolutely the one who is hurt.
There’s no way to learn than to force the brain into adaptation which it is resistant to do through challenge and stress, just like your muscles. Similarly you can’t play e sports and get into physical condition any more than you can use LLMs to do your homework and learn.
It’s going to be a hard adjustment for a lot of people to recognize that letting the machine think for you is as healthy as smoking brain cigarettes.
The smart student uses the LLM as a proctor or provide challenges and feedback on attempts rather than an easy button. They make great tools for learning if they’re used as an adversarial or editorial tool. The future belongs to those who work to use the tools in ways that make themselves more efficacious, not those who use efficacious tools so they don’t have to work.
>The smart student uses the LLM as a proctor or provide challenges and feedback on attempts rather than an easy button.
Yeah, this is how we used wolframalpha for Math as students. Whatever we had to do, we did it ourself as a group of three. Afterwards we checked with Wolframaplha to see if we were correct. If there were any difference between us, we went line by line to find where the error appeared.
It was helpful, because we did it ourself, but because the work was graded, we had the security, that it is not a total failure.
To say that students don’t benefit from getting good grades using LLMs is incredibly naive. Learning is only about the third or fourth most important “benefit” for students, after getting a degree, getting good grades, and making connections.
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The problem with AI in an educational setting is when one is graded versus their students on things and things genuinely depend on those grades. Group projects also force those willing to do things without AI to go along with others in their group who'll use it regardless.
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You're right.
But I like to add artwork to my presentations. My artistic skills have not advanced beyond 2nd grade. So I'll make a line sketch, and give to AI to "fix" it.
The results are nice and I use them.
I have no interest in learning how to do art well myself, so using AI for it is appropriate.
But I still write my code myself.
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When LLMs and ChatGPT first came out, it struck me as obvious and dangerous to a deep thinker or a knowledge worker the answering capacity. So, from my initial use I did not ask them questions, I have always "done my own work" and then asked the LLMs to criticize that work. This has been an exponential ladder of learning, and my cognitive growth is personally noticeable. I'm not hesitating to scribble out calculus and work it out, as I need for my work, where in the past I'd have found some other way because I felt uncomfortable with my tip-of-my-tongue calc skills. Don't ask AI, do your own work and ask for criticism, and them improve your own work yourself. This creates a learning ladder that you will climb.
That's nice except when you work somewhere where more and more developers are pushed to pump out slop generated by AI as fast as possible. So far I am not there yet but I have plenty of friends in the industry who are basically 'not allowed' to code manually anymore.
> many of them can no longer brainstorm, code, think deeply, or write
I believe this is the real crux of the issue. We often turn the target to things like "Can johnny Add, Read a book, or recite dates" which are only proxy measures for important things like "Can johnny solve a numerical problem presented to him, can he synthesize information, or can he think critically about what is occurring around him?" .
If students use AI to accomplish goals I do not see it an issue. If they cannot figure out how to use tools, or what their goals are-- that is a major issue!
An analogy of my point is that I don't want to focus on cursive in the age of computers keyboards, and I dont want to focus on abacus skills when a pocket calculator is like $5.
If students are allowed to use AI to accomplish their goals, then I think the real question is why should they go to an expensive university for four years to learn how to ask AI to do something?
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At this rate humans will become avatars remotely controlled by LLMs. Ironic conclusion of the consciousness debate.
We are already remote sensors and manipulators for the corporate and economic structures we operate under. You can't see it, but we are ants in a superorganism.
More evidence of the philosophical concept of 'technology is a life form.' Humans would be the perfect host, at least for the time being. They are certainly a willing host.
Yes, ask AI to produce a plan for me to do x like design an travel itinerary. It creates the plan and I execute it.
obligatory https://smbc-wiki.com/index.php/2014-12-17
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Distopian, what an insightful frame
What does this have to do with LLMs?
They stopped requiring SAT and ACTs in order to get a student population more representative of the population in general. This obviously allowed students that were not prepared for college into the system.
If you do well in your math SATs you'll likely do well in math college. SAT scores and college GPA are highly correlated. No idea why anyone thought it was good to ignore probably the strongest signal of success in college.
Isn't GP talking about PhDs here? If they're PhDs I imagine they've already had good enough GPAs/were smart enough to be selected for the program.
> Many of them can no longer sit quietly for even 30 minutes just thinking on their own
Plummeting attention spans has been a trend for much, much longer than LLMs and is more the result of constant digital interruptions and these days overwhelmingly social media and doomscrolling: https://www.apa.org/news/podcasts/speaking-of-psychology/att...
The effects on children have gotten most of the, err, attention, but the effects on adults are no less deleterious.
In 2002 I spoke with a lecturer in the humanities and he told me about how nobody was learning French at university level (in the UK). My own course had been cancelled due to the cost of teaching it, and the era of 'easy degrees' had set in during the early 90s.
Before that, I also noticed the decline in newspaper readership in the 80s.
It is easy to blame this general decline on the latest tech (or moral panic), whether that be LLMs or even the existence of the internet, however, the trend in dumbing down has been going on for decades.
In the context of a declining empire and financialised economies, this makes a lot of sense.
I've been wondering if there would be a benefit to inverting how we teach subjects now. Previously we would teach from the bottom, and build up. Semi-colon goes here, curly brace goes there, and then build up to architecture, systems, etc.
But this doesn't seem to make sense when someone comes to a topic with an LLM in-hand. They need to know high-level techniques, architecture, best practice, etc. As they pursue the topic they start to get down into the details, although probably never learn to do it fully independently.
I quite like this view because it paints a somewhat optimistic way forward from where we are now.
You can ask LLMs about high-level techniques, and their answers will usually be good enough. What you can't get from LLMs is the taste and judgment, which you can only obtain by having a strong CS base and coding manually for years.
High-level techniques were never a problem. You could Google tens of articles on this topic. They are useless too, it's like learning how to drive a racing bicycle from reading a book. Sure, you will know a lot about nuances, but you will fail miserably when it comes to a real race.
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I can't speak for other disciplines, but for math and CS, both with a really heavy focus on abstraction, the final result of learning is to build a nice intuition on top of the abstractions we find useful/expressive. And to build the intuition, the old, usual, and perhaps the only way is to see and practice a lot of concrete examples, after which the motivation of building some abstraction can be understood, and after which the abstraction itself can be fully grasped.
e.g. The "group" abstraction requires one see a lot of int, polynomial, modular arithmetic etc. before knowing why we want such a thing. It's unskippable.
This idea sounds good at first, but if you look closer, it would just make workers, not experts who really understand. What we could do, and already do, is tweak the learned abstractions. In our field, it's easy to see: most of us first learned about computing abstractions, not how processors actually work, or started with Java, not assembler. Plus, you can't teach math from top to bottom.
> As they pursue the topic they start to get down into the details, although probably never learn to do it fully independently.
It's hard to claim one has mastered a subject without independent command of its fundamentals. A less charitable take on this future is that students only learn to hand-wave answers and correspondingly cannot evaluate statements beyond "sounds about right".
> Semi-colon goes here, curly brace goes there, and then build up to architecture, systems, etc.
If that's happening, that would be a weird way to teach CS in my opinion.
In my undergrad program, languages and syntax were learned on your own. Class material and lectures were all conceptual.
I keep trying to convince people that English majors and Philosophy majors will benefit the most from LLMs. English majors in particular, have been trained to be VERY exact in how they word things.
That awareness of how to structure the English language, it will benefit those who use LLMs.
Then again, maybe someone will just make a LLM that’s built to turn poor English and poor reasoning into excellent English and excellent reasoning. Maybe this is just a technical puzzle that needs solving.
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I don't think you can learn high level techniques or architectures without first understanding the basics first. This means boring boiler plate coding.
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Yeah. I have no doubt that I would have used LLMs “just this one time” to help with problem sets or papers when I got behind or wanted to do something else.
I have observed this in myself when I began to over-leverage AI in my workflows. I've since become more deliberate with what kinds of tasks I will use it for, although I still slip up.
With writing:
Things like brainstorming a plot line for a book with a custom GPT or Claude project that has all of my prior books in its knowledge? Works great.
Things like asking it to write a paragraph or chapter for me - I can rapidly feel my own writing skill, motivation, vocabulary, and ability to grasp/remember the resulting plotlines deteriorating. I don't use it for that anymore.
With studying:
I've been taking a couple of evening uni courses and the thing I found so great is that I've been forcing myself to think through the problems, and take my own notes in every lecture. I may then still get ChatGPT to help explain and reason through some of the concepts with me. And I have it review and 'grade' my assignments. But I refuse to ask it to start drafting answers.
With programming:
This one is tougher. When I am not very personally invested in a problem or codebase it becomes too easy to offload more parts to Claude, and when the company encourages 'vibing' to speed up velocity and you're reviewing and writing a higher influx of lower quality PRs, investment goes down. I still sometimes catch myself committing solutions I only _mostly_ grasp and the rest is hand-waving. A big part of it is a work culture thing.
For my own projects I make sure to understand and have a back-and-forth with the planning agent for each task, or write the first plan myself to go off of. When it comes to producing the code, I have to admit it is much easier to properly review parts of the codebase I am extra interested and knowledgeable in (backend in my case). The frontend I'm less well versed in and also admittedly less interested in, so I do sometimes fall into the trap of "Ehh it works, just commit it" with the goal of doing a thorough quality pass before actual release.
With all of the above, I can feel my ability to think, plan, reason, focus (and my vocabulary) suffer if I go over the line too much into agent offloading. For me keeping that balance is as much about maintaining my own long-term brain health as it is about producing good output. I imagine younger people growing up with AI today won't even know what that more capable (in my opinion) brain state feels like - to them, the AI-using brain will be the norm.
The place I've come to with AI for writing is to have an idea for a chapter/article/etc, which I take to AI, and tell it to either ask me a bunch of clarifying questions, or try to blow holes in it/challenge it. I'll keep talking to AI and answering questions/handling challenges until the AI runs out of steam, then I'll ask the AI to write out a condensed outline with all the pertinent details of the conversation.
Once I have the condensed outline, I'll re-order stuff, clean it up/tune it up, then do the final writing. This keeps my voice and logical train of thought while avoiding blank page syndrome and some of the organizational mess of condensing notes into an outline manually.
We’re in a world where LLMs are basically going to be extensions of how we think. An additional thing we use to do a lot of thinking tasks.
As a piano player, it’s important to work hands separately. Sometimes your right hand will carry the melody and your left hand the harmony, sometimes vice versa. Sometimes there may be more than just two “voices”/melodies/lines between your two hands. Even as a very good (as in getting paid to do it) sight reader, I learn a lot working all the voices/melodic lines separately.
Singers do similar things like singing only the vowels to keep themselves in the right placement. Learning handstands, you have to work your wrists, rotator cuffs, core (which is many things), etc. separately. Yoga, Pilates, and running also help us learn to break problems down this way.
Anyway, all that to say: If LLMs are gonna be a natural extension of how we think, we need to understand what parts of problem-solving LLMs are good for, and what parts our brains are for. The nice thing about working these bits “separately” is that one side is done for us. So we just need to consciously practice using our brains.
As programmers that means, maybe we conscientiously practice writing things ourselves sometimes. Remembering that this even if this sacrifices short-term “velocity” (whose measurement is problematic, but I digress), it preserves our long-term ability to do good work. And I think any of the above physical/artistic practices (or countless others), worked in these ways, will help reinforce this entire mindset.
I think kids of the coming generation will be sharply divided on their ability to conscientiously practice things separately. It’s been happening, but I suspect LLMs will accelerate it unless how we actually teach kids can catch up.
> We’re in a world where LLMs are basically going to be extensions of how we think
If that's the case then we're in trouble based on my experience. This week I've been using ChatGPT to help figure out some old linux platform that I need to resurrect. It's very good at quickly searching and surfacing relevant information online, and that's helpful, but if I did not have a lot of experience at linux administration to be able to see where it was suggesting the wrong thing, or initially dismissing the right thing, then I'd just be thrashing.
The LLM is helping me because I know what I need, and it can search and read faster than I can. But it's not really very smart.
> An additional thing we use to do a lot of thinking tasks.
Which is to say, an additional thing you're going to be forced to pay a lifelong tithe to a trillion-dollar company in order to do a lot of thinking tasks.
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I dunno, I used wolfram alpha a lot during calculus classes. However my uni didn't require any homework assignments to be done and they did not contribute to grades. Only the exam mattered.
Maybe the problem is that doing assignments contributes to your grades? The answer from wolfram alpha wasn't so much to get the homework done, but to understand how I would be screwed in the exam.
I don’t buy it. Properly leveraging LLMs to generate stable and extendable systems is mentally exhausting (i.e. highly demanding of intelligent thought), especially given the poor quality and churn within the harness ecosystem.
Now, if you’re creating trivial, unstable, or nonextendable systems maybe this doesn’t apply. And maybe I have long overestimated the work that SWEs have done.
i use claude a lot and i find that it is best applied in domains in which i am already a master. I tried applying it to domain's im unfamiliar with and i found that i produced stuff but as time went on i understood what i produced less and i almost felt like i do after binge watching a netflix show, 2 weeks later i barely remember any of the details. I wonder how much you need to "do" to learn and remember. LLM's give you a shortcut to doing and so you probably aren't learning either. It's like when you watch a professor write a proof and it makes sense while listening to the professor but at home you have difficulty deriving it. LLM's give me the same sort of feeling. I think the way forward is still going to be doing things manually to learn and using LLM's once you've mastered an area and people who don't understand this fact are going to slowly descend down a hill and forget how to depend on their own thinking.
As much as I hate to admit it, using agents for too long makes me less able to think for myself. I am dedicating 30 mins to 1 hour everyday writing and thinking without LLMs.
> I am dedicating 30 mins to 1 hour everyday writing and thinking without LLMs.
Before you did this, was literally every hour of your waking time spend thinking about LLMs?
I don't think I could do that even if I tried, and I spend all my development hours with agents, but during meals, showers, walking the dogs, enjoying a coffee outside or whatever, naturally I get time to think about other stuff, sounds out of the ordinary (to me at least) to have to dedicate 1 hour to not think about something. Reminds me of when I was addicted to amphetamines way back when.
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Shouldn't it be the opposite? 1h max LLM per day?
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I wonder if AI or something else changing (developing anxiety, etc) has made the pay-off of the degree less certain. If you're confident that your years of effort will pay off, it's probably easier to see it through. If you're worried that AI will wreck your industry before you hit the workforce, maybe the equation changes and you're more inclined to gamble with shortcuts?
Yes, and this is going to hurt everyone. If everyone knows that you can skate through a college degree without doing any work, it is not going to have much value as a credential.
I totally agree about school-level homework: it was many years before my pre-frontal cortex developed enough that I could have forced myself to do the work.
That said, though, one thing I don't understand about the heavy users of AI in academia and software development is that the thinking and coding is the fun part. And that's the part so many people seem to be so keen to automate away.
I'm right there with you. The thinking and the coding is the fun part. I'm pretty relieved that all of this is happening near the end of my career. To me, AI is just not fun. And constantly signaling how productive I am and having to show "my value" is exhausting. This is only my subjective experience, of course, but in many ways the world seems like the fun is getting sucked out everywhere, not just from AI. Like the type of people that become managers are taking over everything.
Isn't the fun part having the thing work how you wanted it to? Why shouldn't I be keen to automate the process away?
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I've avoided using AI* for precisely this reason. I don't want my brain to get lazy and rot.
(* I can count on one hand the number of time I've used an AI tool.)
LLMs have killed my facility but not my knowledge.
I can still read code and write it, I just need to look back at docs a lot more, when I used to just know things. I also have to sit and try to recall how to do things and what abstractions are involved more. I also have more "writer's block" when starting with a fresh program/document if trying not to get AI to seed it with a baseline implementation, where I have to sit for a while thinking about what I really want to build.
I don't understand why people take shortcuts in school. You pay a LOT of money to be there to learn. Taking shortcuts seems completely counterintuitive to me.
- Time is a scarce resource. Students do what they can to learn what they can, but if they're under the gun, they'll take the path of least resistance to make it to the next day (totally not like the business world, right?)
- In the interest of having well-rounded students, a lot of degree programs include subjects the student didn't want to sign up for, but have to. Even in something like CS, I knew a lot of people who liked the hardware side of it, but didn't like the software side and vice versa. So I can imagine a student justifying taking shortcuts that way.
- Psychological reasons like wanting to protect their ego. Maybe they had always done well in school and are now struggling, but don't want to ask for help, so they think why not just take a shortcut here and promise to do better next time, etc., etc.
A lot of people view it, rightly or wrongly, as paying a lot of money to earn a degree that opens up certain opportunities, while learning is secondary, so minimizing effort is worth it.
And to some people, it's not even a lot of money.
In many ways, schools are just the modern day peerage system.
> I wanted to be in denial about it before but it's too obvious to ignore now
Use-it-or-lose-it is the evolutionary principle, both for cognitive and physical abilities.
before AI was around we blamed Covid for doing this to us, and now we blame LLMs... and before that we blamed social media. I'm pretty sure this downtrend has been happening for decades.
Do any of said PhDs gain anything positive from LLM usage? Or does it only lead to declining thinking skills in your view?
Yes, I can churn out a lot more stuff as can most of my peers. Experiments etc are all way faster to run with coding agents. But I think the overall creativity and originality is a lot lower. I think this is what many people are facing, if you don't use LLMs your short term productivity is worse.
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They're incredibly more productive. LLMs are amplifiers, so where they'd have branched and tried out N things, they can easily try 5N pathways of RnD. LLMs are extending the frontiers of science fast -- math -> phy -> chem -> bio in that order.
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It depends on the field, but an Economist with a PhD is a huge red flag and anything they say should be ignored.
Other fields may be different. YMMV
This is all assuming tests measured anything valuable in the first place. In my experience standardized tests were always flawed and most of my peers knew shit about the subjects they passed in top % a year after. If AI breaks the current education system that's a win in my book.
> Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well
I noticed this before LLMs became a thing. It was by accident. We had a team of programmers. All decent at what they do. The management said 'hey you want to learn another language we are going to be using it for these upcoming projects'. So we set up a self learned at your own pace class curriculum. Maybe 10-20 hours of school work if you sat and really dug in. Maybe 3 to 4 hours if you breeze thru it and do not care much. We set up weekly check-ins doing about 1 hour a week. Easy. Watch a 20-30 min of vid 20-30 mins of do homework come to check-in and talk about what you learned and help others if needed.
Now this is where I was disappointed. The first 'class' was 40 people. By the last there were 3. Those 3 I noticed always are the ones who dug in. The rest wanted a proctored classroom and someone to tell them what to do.
Actual genuine curiosity is rare I think. We have a lot of people who are decent at what they do. But do not really care about it. IF you do not care you are going to just push the button and get the answer.
I could see myself dropping out even if I was interested in learning. I'd suspect that the time spent would end up with me needing to stay late to make up for it or being penalized in some other way.
I'd argue that this is an adjustment period that society has to go through. The way we are using electronic devices today, in some years it will probably be looked at like smoking cigarettes. And I'd argue that a lot of the "decline" is due to a shift of skills away from things that mattered more in the past toward other things that are not measured/perceived by the older generation.
Interesting analogy. I believe regarding addictiveness they may be compared.
> a shift of skills away from things that mattered more in the past toward other things that are not measured/perceived by the older generation.
Do you have any ideas what these things might be? As someone in his twenties, I’m sometimes saddened by observing that some of the skills I acquired over a long time (e.g., writing, coding) may become obsolete or won’t be respected anymore just now that I‘m finally getting good at them.
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Eh, I think it's less like a cigarette and more like the car. We're not going back. Americans are famously less healthy the more car dependent they are, and now people walk/run as an explicit task to be healthy. People will start going to a "thinking" gym, or engaging in additional manual mental activities for sport, like we do with chess today.
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> I'd argue that this is an adjustment period that society has to go through.
I used to think like this until social media proved there are some tech innovations we just can’t adjust to. 10 years ago you would’ve never caught me supporting any sort of age based social media ban. Now? I don’t think it goes far enough. Fake news (actual fake news) and misinformation has only gotten worse with it as well. It’s so destructive.
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I think it varies tremendously from one role to the next. I'm a senior software engineer and LLMs, the way I'm using them, improve almost everything I do. I use them to write most of my code now, but first I spent twenty years writing code before LLMs came into existence and second writing code is like 5% of my job. Most of my job is research, investigation, and architecture. I treat LLMs just like a junior engineer. I give them clearly defined jobs that I could do on my own just fine, that I already spent years doing. The problem here is that students are using LLMs to automate everything BEFORE they become proficient at it themselves. Letting college students use LLMs for homework is like letting kindergarteners use calculators instead of counting on their fingers.
You cannot tell me that letting anyone do something for you does not affect the skills that you outsourced, unless you are some sort of a superhuman.
As an example, I have been drawing portraits for quite a few years now, and whenever I go on a hiatus and come back after a few months, I can notice my skill not being anywhere close to where it was before I stopped using it.
Sure, after 2 or 3 portraits they mostly come back because of the previous experience, but skill rust is a real thing, and if you think your coding skills are the same because you used to code 20 years but haven't coded for some time, you are probably just lying to yourself.
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On the contrary, with the amount of times I went to ask for help and was failed pedagogically, plus not being able to afford tutoring like my peers had, I think access to an LLM would have genuinely boosted my grades.
I still did well, but I had gaps for which there was no help outside of the internet available.
The risk or difference is that tutoring helped people learn which they can use to do the work, whereas with only one or two different words an LLM will do the work (that proves you have learned) for you. A tutor has limits, but an LLM needs to be asked to set limits. And especially younger people are less likely to "punish themselves" like that.
You're using it to help you study and think, which is great, but the original post was about how many people are bypassing that step entirely.
“Better for you if you take me off.”
The Whispering Earring: https://croissanthology.com/earring
I recently switched back from a Tesla to an older car without permanently having a map visible. Suddenly my brain has to think about routes again and it definitively feels like my brain has to put in more effort again to handle it.
We also had exercises for which the solutions were given, and we didn't reach for them immediately...
This likely varies person by person or the way people adapted AI. For me AI replaced the boring part of writing code, but has not replaced the fun part of thinking about code and problem solving.
Maybe you've discovered the great filter.
I used "AI" in the 2000's to increase my homework assignments, and to correct them.
As in I wrote code to generate random exercises, with solutions, using many tricks, to get myself hundreds of problems instead of 1 or 2.
Often spent more time on getting these programs right than on the problems. Still did better than the class. Oh and it was AI in the 1980s IBM sense. Ie. it was based around a python version (which I wrote) of a LISP math system based on maple. I even attempted (and largely failed) to rewrite it in C++.
Even attempted to have my homework read to have the computer correct the actual pages, but I never got convnets to reliably read entire lines (yes, I understand, well now, why a convolution would mostly not realize whether 2 pieces of text are on the same line or not and so get very confused if you go deep enough for recognition to work well)
HN is going to eat up this garbage
The LLMs will gradually cramp more and more adds in their responses in an effort to make a profit.
At least now we know why we will start watering our plants with Brawndo.
Yeah, it's a scary thought. I feel the pull of it every time I'm stuck on a code problem that I don't want to search solutions for and hand-code... and I also feel myself wanting to reach for the crutch of an LLM when I just have something boilerplate and easy to do. It's incredibly tempting to just ask the question and have the "thinking" done for you. Until you have actual skin in the game and realize that it doesn't reason, and its "thinking" is utter shit. Then it's like: you got addicted to cigarettes and now you have to quit, because this habit is poisonous. It really does lead very quickly to cognitive decline if you rely on them, or even think about asking them while you're writing code.
> Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well; many of them can no longer brainstorm, code, think deeply, or write without an LLM present doing 90% of the work. Many of them can no longer sit quietly for even 30 minutes just thinking on their own, which is a required skill for producing original thought.
This was my experience even pre-LLMs though (about my own PhD thinking skills too). I blame the amount of random stuff work now involves more than LLMs.
No, that's the quality of candidates. I wish I was joking as a PhD holder for only 15yr.
A lot of skill of is getting bled into the private sector because getting the PhD in a lot of regions doesn't mean the step up it used to. A lot of that comes from awarding them to layabouts doing "a gender critical analysis of ...".
Industry doesn't how/what/why they just wanted the 3 letters as a performance barrier to hire competants.
You make good points here. But I want to point out some issues that I have with what you are saying, because I see a few assumptions that I would not myself make.
I graduated from RPI with a degree in Management and a concentration in Information Systems. I began in Computer Science, and didn't like it because RPI CS at the time was loaded with professors who were mathematicians who had transitioned over to CompSci and because the 100 and 200 level courses were excessively math-heavy in my view.
Since this was the late 80s, there may not have been an easy way to teach B.S.-level computing without it being heavily math-based, but I digress.
No matter what degree we achieved or what work we ended up succeeding at, we have a tendency to look back at people rising in the ranks below us, see differences in their experiences and struggles, and say, Look! That is evidence of a lack of rigor or a lack of understanding of fundamentals that we had to learn in order to succeed.
The only thing is that some of what we learned to become successful just isn't necessary to be learned when we learned it.
I do a fair amount of low-level software engineering with Claude Code now that was above my level of understanding of data structures and algorithms because I never took those CS courses at RPI because I switched to Management IT.
But as someone who could be described as a solopreneur at some level, my new system designs reach a certain level of complexity or code maturity, and I hit problems that I would not hit if I had more understanding of data structures and algorithms.
So-- I end up having to learn aspects of those disciplines at that point, rather than before I actually needed them.
I run into these situations often enough where I now say to myself, gee, I wish I had taken Data Structures. And I think, could I effectively take Data Structures at this late date and get better at specifying how I want data stored, or perhaps knowing the shortcomings of simplistic database structures that are the ones I end up with initially because of my lack of spec-writing skill?
Aren't many of the less experienced folks who come up now, whatever age they are, going to hit problems that show them their weaknesses in this fashion?
Is the issue that these people will never get jobs because the seniors and managers who are interviewing them will design interview questions that keep people with their level of understanding out of the workforce?
What happens when somebody who sucks at the fundamentals but is really motivated bangs their head against their shortcomings and eventually succeeds in building something that takes off? Aren't those people great assets because they learned some of their critical skills the hard way?
> If LLMs were around when I was a student, I would've also used them to "speed up" my homework assignments then proceed to fail all my tests.
As a counterpoint, I was once a physics grad student. I didn't finish the PhD because at some point I discovered that I was not going to be the next Richard Feynman and this was too much for my ego at the time. But I think that if LLMs were available, I might have finished.
Part of my problem was that at some point the math transitioned from stuff I understood to symbols and notation that I knew how to manipulate but didn't really understand. LLMs could have helped bridge that gap.
On the other hand, it's hard to imagine I wouldn't have used it for Jackson, etc. but we got Jackson solutions from previous students and the internet anyway. Using LLMs probably would have been more effective, used correctly.
This is also where I had issues advancing in math. For so long I was able to build intuition around mathematical concepts easily. They fit in my head and made sense. I couldn’t understand why my peers were so bad and slow at picking up the concepts. Until my first calculus class where there was absolutely no focus on the intuition or practical utility. It was just formulas for the sake of formulas as exposed by our teacher.
It wasn’t until I was curious enough to learn about calculus outside of the classroom that I was exposed to things which helped develop that intuition and made the calculations something other than just symbols and equations to memorize.
I think this would be fine as an adult, if it meant using LLM to churn out the boring work required of you at a corporate gig, to spend more brain cycles on something you actually want to work on.
The problem is that it sounds like many people are just using it for everything.
> For adults the cognitive decline won't be as measurable since there's no exam
I think this is true of every affliction that adults criticize children and teenagers of
I’ve been out of university for a very long time, and I took a community college course and for the first few sessions I couldn't focus or sit still at all. Fortunately I knew that was abnormal and how to conform to a prior version of myself, but I don’t think children have a frame of reference.
I see this too. There is this theme where people are more and more going only as far as the ai does.
Asking suggesting or arguing to go deeper is impossible. There is a new path of least resistance and it saddens me.
>Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well;
tomorrow most regular people's thinking skills will definitely be weaker than those of the LLMs of tomorrow. And physical skills in most cases will be weaker than those of the robots. That leads to the question - what would most people do?
I've got mixed feelings on AI assistance. I'll relate 2 anecdotes.
1 - When I was in grad school (before AI), we had to use Canvas for a class. One day, I got an obvious spam/phishing email in the internal Canvas system. It was so strange. The writer just would randomly hit the capslock button and keep typing away, no salutation, no signature, just a real mess. They were asking for a particular professor to come to their house to teach them about ... something? Again, real strange.
So, I email IT and say 'Hey, somehow a spammer got into the system, do your thing'.
They email back and go 'Nope, it's a student, that somehow managed to CC the entire system, sorry about that'.
Dear Reader, the message was pure garbage. Literally, it looked liked it was written by a 3rd grader without any shame. [0]
I happened to know the professor of the class. So later on, I talked with them over symposium coffee about it. They said that they remembered that particular email because of all the IT back and forth. It was for an upperdivision class in the Engineering department. The email itself was not particularly notable otherwise. In that, they saw such emails all the time, in terms of quality. This was a top 100 ranked (whatever that means) university, by the by.
Shocking.
2 - My grandfather was an officer and a mechanic for the USAF. A bit of an odd combo, but he was partly responsible for instituting many preventative maintenance checks and protocols, novel in those early days of the AF. His aptitude and memory were quite sharp for many mechanical things. Until the strokes from decades of smoking caught up, he could tell you exact measurements and torque values for a variety of airplane related things (I can no longer remember what exactly, the memory skills did not transfer to me).
I do vividly remember standing in that light blue garage of his and him all but yelling at me once. We were looking at the brakes on an old car he was 'restoring' (getting away from Grandma for a little bit). He pointed at the old drum brakes on the axel.
He asked me how tight the pads should be on the inner rim of it.
I had no idea.
So he asked where I might find out.
I figured I'd ask him.
But what if Grandpa wasn't there?
We'll I'd have to look it up somewhere (they had no internet).
Fantastic. Now, what about the next time you're working on the brakes?
Well, just make sure that the pads are at that spec.
And that when Grandpa hit me with the nugget of hard won wisdom: No, you look it up every time. Because these are brakes, and if you are wrong then they might fail, and they might fail when the driver has their whole family in the car at 100 mph. And then because you were lazy, half a dozen people die.
---
These two times stand in my head when it comes to AI.
For the first one, yes, AI would be such a boon to that very clearly struggling student reaching out for help. It would get them back on the path to the real struggle of getting their degree. That level of assistance would be like a wheelchair to a paraplegic.
For the second anecdote, AI is condemning people to death. Using it in life critical situations and care, letting it hallucinate or skip over critical values, that's a recipe for disaster.
Where do we set the fine line of using AI and not? For brakes and X-ray machines, obviously not. For helping kids learn to write emails correctly? Sure, sounds great.
Unfortunately, I feel the old adage about regulations is going to be true here like it is with every new technology: The rules are written in blood.
[0] therh lasdkjenail EMAil was sposmkehting alLIKE HTIS IN THE WAYS THAT IS aw adklhjf writtene sand htenn. sned t sent ASLAOSNG ASLIONG ALONG TO NTHE recipentnts .
How many took the COVID vax? /s
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> Many of them can no longer sit quietly for even 30 minutes just thinking on their own
Sorry, but I highly doubt that. Has a very "old man yells at clouds" vibe.
I doubt that you doubt that. I think you actually believe they're speaking truthfully and in good faith.
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I feel the opposite. My brainstorming has increased rapidly. I can now just throw ideas at an LLM to rapidly validate.
Are you saying you leave it up to the LLM to judge whether your idea is good or not? Are you even human anymore?
(I am not saying LLMs can't be a good tool in evaluating ideas. To me, it sounds like you're firing off ideas all over, letting the LLMs judge what's good and what's not. Insane.)
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When reading and writing became prevalent, the ancients bemoaned our reduced facility to memorize long texts. Are we now “less smart” because of that technology?
The likely 'real' reason is hidden in one paragraph within the article and has nothing to do with the implication of the eye-catching title: "Both Garcia and Ranade have joined more than 1,300 UC faculty in signing a petition calling for the reinstatement of ACT and SAT standardized testing scores for STEM admissions in the UC system. The petition and its accompanying open letter detail similar concerns with students’ mathematical preparation."
Around COVID times many top universities experimented with removing test requirements from admissions, under an argument largely related to equity. It's been a failure everywhere, with many, if not most, universities already reversing it. As Yale put it, "Yale’s research from before and after the pandemic has consistently demonstrated that, among all application components, test scores are the single greatest predictor of a student’s future Yale grades. This is true even after controlling for family income and other demographic variables, and it is true for subject-based exams such as AP and IB, in addition to the ACT and SAT." [1]
That link is for an archive because that page has been removed. That's because they briefly experimented with a new 'test flexible' strategy where they allowed students to submit test scores or not, but then scrapped that altogether and went back to simply requiring test scores.
[1] - https://archive.is/8zxfo
Berkeley chancellor told students to vote for 2020 California Proposition 16, which would've repeated 1996 Proposition 209 that banned race-based admission in public universities. Prop 16 failed. Subsequently, Cal started ignoring SAT/ACT scores. I have to think this was their alternative way of taking fewer Asian students, who average highest on that. Soon after I got an email from the same chancellor praising the change for bringing more racial diversity. The email included before and after numbers where % Asian decreased and all others increased.
Reminds me of this asian professor getting blocked for promotion, allegedly, on the basis of race, but in a roundabout way. https://www.msn.com/en-us/news/us/professor-sues-texas-unive...
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They could have easily made test scores a pass/fail per program and not weight higher scores for admission purposes. It achieves the goal of ensuring students have requisite knowledge for the program while not favoring students who are able to ace the test.
Or, even better - just expand programs so they can accept more students who pass the test. This would probably improve diversity without artificially restricting access to highish performers.
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(minor typo: repeated --> repealed. Sorry for the nitpicking but it confused me when I first read it)
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If the removal of standardized testing in 2021 was the real reason, then why is there a sudden spike of failure rates happening right now?
It takes time to work through the system and it has been steadily getting worse.
It was already discussed on HN.
https://news.ycombinator.com/item?id=48309233
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I'm guessing the kids who didn't do the standardized tests at/shortly after 2021 were already prepared for it.
The kids who saw the removal of standardized testing 3 years out from going to college never bothered.
It takes time for students to work their way through the system.
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There's always a lag between cause and effect in education.
Works the other way too - if you introduce something positive in grade 1, you'll only see the results a few years later.
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Testing was the annoying flood barrier. AI is the rainstorm that shows why it was necessary.
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I'm not American so maybe I am missing some context. But how did admissions work without test scores?
It varies by school. I went to a (low ranking) state engineering school and it was guaranteed entry if a prospect met the following criteria:
- Had high school diploma (or equivalent).
- Resident of the state for >6 months (student or one parent).
- ACT score of something like 21. With provisional admission granted to students with scores below, until they completed all first year engineering courses with a B or better.
So likely they just dropped the concept of provisional admission. All that did was open up classes for registration a week later to ensure other students were able to get their preferred class openings. Provisional had to take the scrap classes, like the four-hour, once a week Calc class on Friday night.
Not American either, but in the US many schools use/used standardized admission tests (SAT/ACT) on top of things like HS GPA/grades.
There are many countries, especially in Europe, where entrance/admission tests are not a thing.
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They look at high school transcripts and the application essays. I don't know how they decide based on those.
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> top universities experimented with removing test requirements from admissions
What could go wrong...
But do these universities not have math placement exams? Not for admissions but just before you register for your first semester classes, a 30 minute math test should be a straightforward preventative measure. I did a test like this, I assumed they were pretty universal.
They do -- this is often how they've found that students needed additional math coursework before starting the standard curriculum.
Memorize trivia and formulas, regurgitate trivia and formulas. This summarizes my experience with our system of education. Yale saying test scores predict performance reads to me as, “students’ history of being able to regurgitate trivia and formulas in high school is the lead predictor of their ability to do so here.”
> removing test requirements from admissions, under an argument largely related to equity. It's been a failure everywhere [...] among all application components, test scores are the single greatest predictor of a student’s future Yale grades.
It reads as though you tried to use the quote to support your conclusion that "it's been a failure", but the quote and the original rationale are optimising for different things. Something can be a success in improving equal opportunity while still leading to worse grades.
Or to flip it around: we could say admission testing "has been a failure everywhere" because it biases admissions in favour of certain demographics. But that wouldn't really be a fair assessment because being free of demographic biases is not the purpose of admission testing!
CS Professor here: just yesterday I did the discussion of a course projects' (Parallel Computing), and one of the three groups that I did yesterday have clearly gone the ChatGPT way. They couldn't even understand the choices the LLM made regarding the architecture, etc. The way to "catch" these students is similar to what we did in the past when students copied from other students which is "to give them rope to hang" - ask for clarifications until they follow unintended paths that lead nowhere.
To fellow professors, when you're suspicious my suggestion is to appeal to their honesty (like "let's be honest, how much of this code is yours, and how much is ChatGPT's?") and offer some empathy and understanding (like understanding they may had multiple deadlines in the same week, etc.). Nevertheless, don't miss the chance to give them the lesson on how is the correct way of doing things. The way to catch these students is to find the same signs of yesteryear copying from other students (which in essence is what copying from an LLM is, although the number has increased because they found us professors unprepared for the volume).
The other two groups also used LLM but in a high-level and architectural way. They were clearly responsible for the code (even if they didn't wrote it 100% manually) and could explain their reasoning and strategies used to solve the problems.
Me and my colleagues still have a lot of projects to review, and I asked them to keep the score of the number of projects like these, but so far, the score is 1 in 3 (33%).
As a professor, what's you opinion regard the Socratic method with LLMs? Would that be preferable over simply "give me the answer" prompt?
Everything that provides students with a workflow to think and to try to find solutions to a problem is much better than giving the answer directly! Unfortunately there will always be students that prefer to take the shortcut..
How could we "force" the students to use an LLM that confronted their doubts with more questions? We could tell them to start each chat with a specific prompt (to use the socratic method, etc), but they could eventually jail-break it..
But nevertheless, I like your idea! This is something that a document highlighting methodologies for students on how to use LLMs effectively could/should contain..
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> The other two groups also used LLM but in a high-level and architectural way.
Sounds more like the score is 3/3 (100%)
Would you have accepted them cooy-pasting code from libraries together to build their project? If not, why is using LLM generated code different?
Using AI to inform architecture doesn’t seem so different from googling architecture in this case. Architectural patterns are mostly well understood and well documented these days and are something that you could piece together via Google search pre AI. The thing that AI brings to the table that wasn’t google able in the past is code generation. Previously you had to understand the architecture patterns to implement them yourself, but now the AI can just do it for you.
> Would you have accepted them cooy-pasting code from libraries together to build their project?
Yes, if they are "responsible" for the code delivered, where responsible means they understand the code, the architecture, the decisions made, etc.
In this case, the students had to invent multiple strategies to solve a specific problem. The "successful" groups did a mix of generated and hand-crafted code (don't know percentages), implemented different strategies and knew their plus and minuses, could change the code in a timely manner to accommodate some of my requests, etc. The "unsuccessful" group couldn't do any of that.
I'm not anti-AI (and really, what could I do if I were?) since I use it myself, I'm just anti-slop, especially from my students.
But in reality I've been slowly transitioning from group projects (for a subset of the grade) to "practical tests", where they must implement a significant subset of a larger project in a 2h class. Still experimenting though.
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He explains why in his comment. Read it again, carefully. Or ask an LLM for an "explainer"..
What is the policy and guidance you gave the students regarding LLM use?
This is all so new, and caught us completely unprepared that there's no official university-level policies. Most of us are still navigating the waters and seeing what works and what doesn't work anymore.
I have colleagues that are teaching for more than 30 years, few years away from retirement, who suddenly have been confronted with a new way of doing things. Those are the ones that are still insisting on doing practical projects, etc. I've only been doing this for 20 years, and I'm quite lazy (worked previously as software engineer), so I've moved to those practical tests. I guess that there should probably exist a class or workshop to teach these students how to use LLMs effectively, but as I said, this technology and its implications is quite new.
Personally, what I did was to give them the "lecture" in the line of that they do not understand what the machine has generated, that is not the way a true engineer does, try to do some parallel with things like an LLM designing a bridge and civil engineers building that bridge, and a fatal flaw collapsing the all thing, etc.
In other words, we do not have a formal system in place, it's all talking and convincing them. Obviously it's a big enough problem that should deserve more investment in solutions, but we are all overwhelmed by other tasks. Maybe LLM studios should be held responsible for all these "disruptions" and provide solutions to problems they created! :)
It's a strange thing that as humans, we sleepwalk into every crisis, never agreeing on anything, and then when we're there, we also never agree on the causes. When we ge too the point where we can no longer "engineer" or "science" anything we will spend the next decade arguing that the issue was not really AI, or that if it was, it was inevitable and no one (or everyone) was to blame. Rinse, repeat. Yet we're here, today, looking at the bleak future, and taking yet another step forward.
Do we assume society just self regulates. I think it does, but the cost of letting it self regulate is really really high, with lots of suffering. Is it that we find this acceptable when there is a chance we won't be the first to feel the pain?
People have been warning about AI coming for decades. For better or worse, it's embedded in popular culture, in science fiction books and movies. But that's different from figuring out practically what to do.
It's cultural evolution and it's how markets work, too. You were expecting central planning?
https://i.imgur.com/fk1aI.jpeg :)
Yes, climate change debate went the same way.
We don't all sleep walk into these. Many people shout from the roof tops but the masses are easily manipulated.
They worry me. A lot.
My son is 15 and I use Google Family Link to control what he does on his phone: it's pretty open for the most part (I receive notifications of installs) but Gemini is a hard-ban.
We've spoken at length of the dangers.
He says his pals use LLMs frequently and I suspect that's the reason for their test scores: some of them are in the 20% - 40% range for tests whereas my son is 80%+ because he studies past-papers and answers questions in his revision.
I worry for the future coz you can be sure that the AI providers don't care if a schoolchild is using their LLM to answer the homework questions.
Did your son behave in a way for that to be installed or did you do it by default?
> Gemini is a hard-ban.
Sounds like you would hard-ban your son from using Internet if it was only introduced 5 years ago
Or a calculator. Oh wait, I hard ban my 8 and 11 year old kids from using a calculator. I wonder why.
In unrelated news
"More than 600 University of California faculty members, led by mathematicians at UC Berkeley, are calling on the system to reinstate standardized testing requirements for science, technology, engineering and mathematics applicants, saying that six years of test-free admissions has not reliably assessed readiness and professors are often teaching middle school math to incoming students."
https://archive.ph/18spS
Who even makes the decision to drop having a standard bar to verify students?
And what possible benefit would that have?
The book SAT Wars has arguments for and against and the striking thing for me was that some in admissions believe in a concept called crafting a class: the applicants are input into the admissions officer’s artisanal contribution to producing a class that they believe would be good for the university to have.
The idea of a standard bar and so on does sound like it would interfere with such a process.
I always did find it interesting that US notions of anti-racism required treating individuals not as individuals but as racial representatives. It’s a local quirk of the culture of the land, I suppose, that one’s primary identification here is one’s skin colour.
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Why else does anyone in California get rid of anything? Because it’s racist.
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The decision wasn't specifically to drop a standard bar. It was to drop the existing bars because they have become heavily gamed and are far more reliable indicators of your family's resources than your ability or likelihood of success. That was the equity argument.
Unfortunately, the lost signal wasn't replaced with anything. (I don't know what could replace it. It's an incredibly hard problem. )
I thought for most universities it was because some schools didn't organise the tests and others did during COVID.
More leeway in who you accept (for money and/or clout).
Hard to find the actual letter: https://ucstudentsuccess.org
My daughter was struggling with her Math class back in January. I used Claude to build a tool that allowed me to generate very focused worksheets. The worksheets had problems designed to drill the concepts she was struggling with.
It worked, and it would have been MUCH harder to do this the traditional way.
The tool generates PDFs including an answer key and solution sets that solved the problems using a variety of techniques so I could check her work more easily and we could iterate quickly.
That's powerful. It comes back to how are you using the tool. Are you using it to make things better or to take shortcuts?
>In addition, the guidelines state that “a typical GPA for a lower division course will fall in the range 2.8 – 3.3.” In spring 2026, both classes’ average grades were C-pluses, according to Berkeleytime, corresponding to a 2.3 GPA.
As a Cal alum, I am actually really glad to see they are holding the line on grade inflation. I worked my butt off to achieve the GPA I did, and it would really suck to see my labor devalued if Cal went the direction of e.g. Yale and started handing out 79% A's and A-minuses: https://yaledailynews.com/articles/professors-face-grading-d...
I read the subreddit for the UC I went to. When acceptance letters went out this year there were (as you expect) a ton of questions from accepted students. About 1/3 to 1/2 included questions about how bad "grad deflation" was, asking for comparisons to other campuses.
Unfortunately grade deflation has little positive impact for the students. Medical and law schools often (typically) don't take grade inflation/deflation from a school into account. And almost no scholarships take this into account. If you do have professional school aspirations, there's very little benefit to being at a school with grade deflation.
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It won't last. You need a good grade to find work after so handing out lower grades decreases applications next year.
On the plus side, high grade + long ago remains a signal.
I doubt that's the bottleneck. UCB's acceptance rate is not high (<5% for CS). They have way more people who want to get in -- qualified kids, too! -- than they can fit. They'd need to burn through that backlog before it started showing up as a signal.
Unpopular opinion: turning public universities into an academic hunger games is diametrically opposed to their purpose for existing, which is to create an educated populace. Intentionally lowering the quality of instruction, as well as deliberately trying to trip students up on exams, is not improving educational outcomes for anyone. People who complain about "grade inflation" have completely lost sight of why public education exists in the first place.
Obviously a balance would be best, but as someone who went to a very grade-inflated school, I do believe that grade inflation gets in the way of education substantially. When you can get through classes with very little effort and understanding and know you will get a sufficient grade, many people will simply not learn the material deeply.
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Some of the exams in Berkeley were brutal, but they never felt like trick questions, they did on occasion require a level of mastery of the material which was extreme, but it never felt like someone was just trying to make the questions obtuse for the sake of it.
Even more unpopular opinion: universities don't exist to create an educated populace. People don't need universities to learn, they can read textbooks on their own.
Universities exist as gatekeepers and credentialing bodies. Their purpose is to certify that a person has studied some topic in depth and is an expert in it. They promote education indirectly, by giving people an incentive to study.
A good university is one where anyone with a degree is guaranteed to be highly knowledgeable in their field of study. This makes it easier for anyone who might want to employ or do research with graduates, as there is no need to test their knowledge.
By this metric, universities have failed spectacularly. This is particularly obvious in computer science. Employers routinely ask CS graduates to solve data structure/algorithm problems in interviews, because a degree is not enough to prove that somebody knows this stuff.
Except this is exactly the opposite of turning it into the hunger games. That would be a situation where failure is kept artificially high by high-grading/curve. This is not that.
No one is intentionally lowering the quality of instruction or trying to trip students up. They are trying to get them to pass the same bar that generations of students before them passed fine...
There are 10 different public universities in the UC system and 23 in the CSU system. The majority of them are not difficult to graduate. If you don't want a demanding education, don't go to a demanding university.
>Intentionally lowering the quality of instruction, as well as deliberately trying to trip students up on exams
I was happy with the quality of the instruction, and I didn't feel I was being "tripped up" on exams.
It's not about "hunger games", it's about challenging students to learn a lot of material and learn it well. Again, if that's not what you want, just don't attend.
The number of places where this environment exists is getting smaller every year: https://xcancel.com/CJHandmer/status/2060144837157118307#m
I'm glad the professors at Cal are working to preserve it there.
This is valid, and I would add that these academic hunger games are a result of College Degrees being needed to get what remains of well paying jobs.
Maybe we can use AI to create new exams that grade people on professional capability, and then gate entry into other professional degrees?
Hmm, Where would the teachers come from, and how good would the education actually be?
Writing better exams, even if they're more expensive to grade, and removing homework from grading as far as possible addresses this problem well wherever it's applicable. Senior-level math courses at many universities are already like this: homework is ungraded, or counts for little, and it's possible for students to "cheat" on the homework by copying another student instead of struggling through the exercises. But the students who do that don't learn much, if at all, and predictably fail the exams. Professors warn students at the beginning of the class and tell them how this will work, something like:
> You can always ask me for feedback on your homework and I will mark up every part of it, but you won't receive a grade for homework. However, if you don't do the homework and take your time with it, you will fail the class. My office hours are in the syllabus and you're strongly encouraged to use them. There will be an early exam to give you a chance to know whether you are likely to fail this class before you lose your chance to drop it.
Correctness is harder to adjudicate in some humanities disciplines but the format of these exams is actually not super different from essay tests (when a math professor grades a proof, they're inspecting specialized prose for validity, coherence, persuasion in a way that also reveals knowledge).
When you don't rely on homework for determining whether or not a student passes the class, you make cheating on the homework into the student's problem instead of the professor's or the university's. Students have the right incentives to solve problems for which they are the ones responsible, and they figure it out after one failed (or ideally, dropped) class at worst.
Well here we have a product market fit issue. See the market was lead to believe that if you got a degree you got a job.
So learning was never the actual goal.
Originally, at least in premis, it was to learn and advance the arts and sciences.
So what we need now is a college for llm's to advance the arts and sciences.
Pity. I recently started a fun activity to rebrush my math my where I tries to solve problems while asking Gemini Live mode for confirmation and suggestions, sometimes step by step.
It kinda was fun, like a very patient professor stand right besides you. It was the one of the best math learning experience I've ever had, and you don't even need to send bribe/gift to Gemini to keep you in it's favor.
On the other hand, if you ask a LLM to completely finish the work without thinking it through by yourself, then it sounded like cheating, to yourself.
What a terribly ambiguous title. "Failing grades soar after xyz" makes it sound like xyz has helped what were previously terrible, failing grades become good ones.
Failing news headlines soar after AI takes over reporters jobs.
*quietly takes over reporters’ jobs
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No matter how many times I read it, I can't interpret it the way you're suggesting. "x soars after y" always reads as "x increases a lot because of y". I don't really get what you're saying.
Are you maybe saying that "soars" might mean "get better", so "failing grades soar" might mean there are actually less failing grades? That's not how I've ever understood that word.
Imagine an elementary school teacher told you that many of her students had failing grades, so she had implemented a new reading curriculum.
If she told you that afterwards the failing grades had "soared", it could easily be read either way:
- The (previously failing) grades had increased, so the program must be working very well.
- The percent of grades that count as failing had increased, so the program must actually be terrible.
"Falling" means that something goes towards the earth. "Soaring" means the opposite. "Grades soar" means that grades went up "Falling grades means that grades are going down". "Falling grades soar" is just meaningless writing.
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I suspect the ambiguity might be part of making it "clickbaity", as it naturally causes you to wonder which meaning it's about and become more interested in reading.
It's incredibly difficult at this point to "skate where the puck is going" as Gretzky is said to have done. No one knows what knowledge work will look like in five years. People used to memorize log and trig tables, and no one would say that's part of being a competent mathematician at this point.
That said, assessments of poor critical thinking skills jump out at me more than the rest. That sort of thing seems likely to matter until machines can replace us completely.
> People used to memorize log and trig tables, and no one would say that's part of being a competent mathematician at this point.
Sometimes I don’t wonder if this wouldn’t still be a good way to educate people. Part of the problem is education has to sort of optimize to try to educate like passive people. If you’re a curious and pragmatic person, you can understand how to use what you learned in a liberal arts degree to be better at almost any job.
As I look forward to the second half of my career. Certainly I use AI in healthy doses.
But people talk about the division between practice and performance, and most of my practice is old school. Reading books. Writing my thoughts down. Memorizing quotes and passages.
I think more important than what you learn is the way you use it to train and evolve your brain, with the caveat that - I know this is more useful to me because I have a marketable skill. This is the balance universities have to stick, there are tons of people with liberal arts degrees in middling jobs.
But at least half if not more of education should be on building practical skills in the three r’s.(maybe the third r should be ‘rgumentation instead of ‘rithmatic, but I digress)
It’s interesting - people decry memorization in education, and I’m not entirely naive as to why - if you were to show up to the first day of work and say “I don’t know any of what you just said but I can recite log tables! It might be your last day - and yet one of the most underrated skills, especially late in your career is the ability to ingest and operate in large quantities of information.
> People used to memorize log and trig tables, and no one would say that's part of being a competent mathematician at this point
Do you have evidence that it ever was part of being a competent mathematician? AIUI the trope of mathematicians who can't even do arithmetic was common already before the pocket calculator was introduced last century.
>People used to memorize log and trig tables, and no one would say that's part of being a competent mathematician at this point.
That would be closer to engineering or accounting than mathematics. I don't think mathematicians do much arithmetic at all.
Go read the story that Richard Feynman tells of betting an abacus user. He used his knowledge of some strange numbers. It's in _Surely You Must Be Joking_.
I suspect his facility with numbers and his knowledge of tables like this really helped him do physics research.
See also his stories on approximation.
> That sort of thing seems likely to matter until machines can replace us completely.
This seems like the crux of the issue. Like people are banking on that day coming even if they don't know exactly when.
The obvious cognitive deterimental effects of using map apps, when we all realized we lose directional sense and our previous ability to navigate without the smartdevices, was society's canary in a coal mine and a headsup of what was coming.
WAL-E and Idiocracy. The future.
This goes with calculator to do basic math, contacts apps to store phone numbers instead of remembering them, and watches to tell time imo.
Sure we could use our brain power with old techniques to do these, but why? I don't want to do any of these. I'd rather use that brain power for other problems.
Same with maps.
I don't want to have to store a bunch of location or routing data in my head.
I think what you're pointing towards is going from having problems to solve to not having any problems to solve.
That's definitely a danger, but right now is still early in the AI era so obviously it'll feel like we went from solving problems to letting the new tool solve them for us.
There are still many problems to solve.
You are confusing faculty with records. The ability to navigate by sense of direction. The ability to memorize numbers. The ability to think clearly by yourself.
Watcha gonna do if big tech takes away your access to the outsourced brain, dear?
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> I don't want to have to store a bunch of location or routing data in my head.
hmmm, given how closely memory is linked to spatial navigation sense, and not just in humans, but in evolutionary terms-- think squirrels remembering where they buried nuts, birds and fish remembering migration routes, ...
suggests the ability to store location/routing is foundational to much of intelligence.
Even simple tasks, typing, for example, depends on my knowing where the keys are. Imagine if your keyboard re-organized its keymap randomly every third keystroke.
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> I'd rather use that brain power for other problems.
Except you won't have the underpinnings to even properly think about other problems. Your brain will be mush.
> I don't want to have to store a bunch of location or routing data in my head.
This is preposterous.
You are not storing these things in your head at the expense of anything else. You’re “training” the existing underutilized capacity in your brain for something useful.
> “I’m a strong, strong opponent of what Harvard is doing to say that only a fraction of students can earn A’s,” Garcia said. “I think you should have clear standards for what an A means, and then give tons of opportunity for people … to get to that A bar without lowering the standard. So everybody who’s curving is hiding that effect. It’s completely hiding that effect, and it’s pretending as if nothing’s wrong, and something is definitely wrong.”
Grade curves are how you test your curriculum for good challenge - are you challenging people such that an A isn't a too-low threshold. When you force people into a curve, you haven't defined a threshold of mastery, you've defined a sorting function: A means "better than this year's peers". It is absolutely bananas to me that a tech/math oriented school would be doing any sort of curving.
I think curving has its place. One of my math professors explained that in his opinion an effective test should differentiate performance as much as possible. The top students should score very well and the bottom students should score very poorly. If all the scores are clustered near the top (>80% for example) then it's hard to tell who really mastered the material and who just muddled through. Then, once you've sorted the students you can apply an appropriate curve. He did not have pre-defined thresholds, for each exam he would evaluate when he felt like the quality of work changed from an A to an A-, A- to B+ etc. The curves were very fair; he wasn't trying to force some number of As Bs or Fs, but it did increase my stress levels not knowing in advance how well I needed to do on each exam
Yes, curving has a place here - and it is to evaluate, as you put it, whether the test differentiates performance as much as possible.
If you curve the students after the test, you are applying subjective edits to the graded performance just so the distribution of grades matches the measure of your tests effectiveness. That's just hacking the metric.
Further, even if you believe that tests should differentiate mastery (not students), your test should have teased out the differences or given you enough confidence to provide As to everyone who mastered the material - which should be absolutely possible! There's no a priori reason that all students cannot absolutely get the same grade, except for the a priori assumption that grades are for differentiation of students themselves (this year's A means this is the best student of this year), vs indicating mastery (all students absolutely crushed this exam).
You can dock points for style, or unnecessary struggle, or whatever subjective metric you want, but fudging the grades based on vibes to fit a prior-assumed distribution is just kinda "test effectiveness laundering"
But when there is no standard and students are subject to tests created by and graded by egotistical narcissists, a curve can be the only way anyone passes the course at all. It simply wouldn’t be acceptable for 5 of 50 students to pass because professor egghead can’t write coherent question on a test.
I had classes where I didn’t make over a 50% on any test and still got an A because half the class dropped and the other half hung on for the curve like I did.
I think curves are more a result of poor teachers than anything.
> I think curves are more a result of poor teachers than anything
Precisely right - that's what I said, too. You fit a curve to see if your coursework/exams fit the students. But you don't fit a curve to ensure that "precisely 10% of the class gets A, 20% gets B" etc etc. If you dont like the grades your students are receiving, you either fix the coursework or the students.
One thing I’ve used in interviews is to write some code that looks like it was written by an overly enthusiastic engineer who just discovered some new concept (e.g. “trees are the ultimate data structures”) then have the candidate review the code. I wonder if this could work for education: orient the entire class around who can give the AI the best corrections.
It’s not just students; this affliction is cropping up among established academics. My wife is editor-in-chief of a journal and in some months has rejected 100% of the letters to the editor including 6 that came in from a single author because all scored 1.0 certainty of complete LLM fabrication. The author in question is no student. It’s a little more difficult to fabricate an entire original paper this way, I suppose.
It will have taken us less than 1000 years to go from scarcity of the printed word to the over-abundance, and finally to the uselessness of it.
One of my favorite jokes ever is from a dear friend who happens to be a graduate of the Berkeley CS program: "Programmers don't need to know how to do math, they only need to know how to add 1 to something."
It's interesting that it's specifically math-within-CS being discussed here. I can imagine a lot of students "just want to learn programming" (or similar), and see the math as a tedious distraction.
As a naturally curious person, nothing will stop me from learning about the topics that interest me. But school also taught me a lot of things that didn't interest me, and a lot of those things turned out to be useful anyway. I think if I had access to AI from a younger age, I'd have used it to skip learning the things I didn't care about, which would not have done me any favours.
There's some discussion of math skills in the article, but the headline courses with huge jumps in failing grades (CS10 and 61A) are pretty math-light. The former is "CS for poets," the latter is the first CS class for majors - lots of work on scopes, recursion, basic data structures, and, at the end, a simple Scheme implementation.
Understanding math well might help a bit, but they're the least mathy classes in the core Berkeley CS curriculum IMO.
I also think that's one reason.
Where I'm from (Norway), the majority of computer science and software engineering studies do not have the same math requirements as, say, engineering or math/physics/etc. - nor do they have the same amount of math as the latter ones.
When I did my CS classes as an engineering student, I did meet a bunch of students that viewed math as some niche subject only relevant to those that wanted to work with computer graphics, computational stuff, or similar.
My (UC) CS (pure software) program required a bunch of math, but not for the math. You could talk almost anything (I did set theory and meta-logic), it was required to ensure a certain level of mathematical formalism and reasoning. Which is very helpful in CS.
Personally, I do believe that math as a discipline has this huge issue of being mostly incomprehensible garbage.
Not because the actual truth encoded in it would be this complex, but because the encoding scheme just sucks.
I see it as a packaging problem that has so far not been painful enough to trigger any meaningful change.
With this LLM-driven collapse, that might finally change.
Idk I'm hopeful.
Math is literally the law of the universe. It makes zero sense that the way that it is taught needs some special brain wiring only found in small chunks of the population to truly click.
> Math is literally the law of the universe. It makes zero sense that the way that it is taught needs some special brain wiring
Ok, I'm all for overhauling math notation and teaching but this doesn't follow. Most animals can't do Math, even if they can do arithmetic. Clearly living in the universe doesn't guarantee you can learn how it works. There's no reason to believe we slightly smarter animals are universally entitled to understand it either.
Back in the university, I took both math and CS courses and a significant percentage of students seemed interested in neither math nor programming but rather in the jobs they would get afterwards. I didn't notice the same thing with math majors.
Well, that’s because for math majors any monetary incentive is nonexistent (modulo some rather specific careers). Just about nobody majors in math for any reason other than math itself.
If you watch old videos of tradesmen using basic hand tools like hammers, you'll find examples of skill/dexterity with the tool that I think don't exist today at all except maybe in communities like the Amish.
I think it's true that we collectively lose something akin to beauty every time technology advances. But usually some new set of skills that have beauty emerge.
If LLMs end up being the pneumatic nail gun for the human mind, I personally think that's a fine thing for us to accept.
If they end up being more like some dark factory that autonomously does everything - then I think ultimately the thing that makes us human (our minds) will slowly decay and be lost, and that seems very sad. That's a version of the future we should try to prevent, I think.
It seems pretty clear that LLMs are going to be extremely corrosive to culture in general.
Unfortunately it's already time to remove the "going to be" part of this.
No more corrosive than introduction of internet
I mean the argument that is being put forward is that it isn't a pneumatic nail gun for the human mind - it atrophies are mathematical capability and quality of understanding.
Right - but you could imagine a similar sentiment anytime a skill was replaced by a new technology.
I think the jury is still out on whether LLMs actually lead to complete atrophy of skills that don't eventually get replaced with brand new skills.
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You are less of a human for not starting all of your fires using friction from rubbing two sticks together. People who use lighters are destroying their ability to start fires without lighters and that is a very serious problem!
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A famous MIT professor did a sabatical at our AI lab. He said it was "a joy to teach here, as you can rely on students being proficient in basic math as opposed to the US where you have to teach those explicitly or lose the class completely".
That was in the 1980s.
My first math exam as a CS undergraduate, 123 out of 129 students failed. The math department professors refused to dumb down their classes for CS students.
Math was core to the CS curicullum in those days. It would fade away over the next few decades to almost nothing. The main reason being the CS department wanted to popularize its uptake, and remove barriers that kept students from passing. There was also a major dose of interdepartemenral rivalry and academic politiking involved.
To be honest, there’s approximately zero reasons to teach major-grade math to just about anyone but math majors. None of the applied math disciplines need go that deep, and what they do need depends on the field (physics is all about analysis, CS is about algebra and discrete math, and so on).
My CS program required one year of upper division math. But you could take anything (I took set theory and meta-logic from the philosophy department, it was actually pretty hard!). They did not care about the specific math skills, they wanted us to have a level of mathematical formalism and reasoning, which was in fact important for the CS classes.
In terms of material learned maybe, in terms of shaping logical thinking and tackling hard problems there is a huge benefit.
These things are all true but in the end the most transformative AI results came from US labs with US university trained students, so one must ask what the purpose of a more difficult pedagogy is if it doesn’t lead to humanity’s greater knowledge.
Can I ask, just out of curiosity, where the AI lab was?
Brussels.
While this is worse in degree, it’s not new.
I TA’d in the early 2000s and the first day students were warned that we used automatic analysis to find programming assignments that were similar to previous submissions. And renaming things, moving them around etc would not help.
We caught and failed cheaters every term.
AI has a way of exposing people. In this example, students who are there to get a degree from a prestigious institution, rather than to learn, are prone to take perceived shortcuts and proceed to come unstuck when their AI isn't there to do their work for them, such as in an exam.
It's too damn tempting to not use. You have a magical machine that, on command, will spit out the answer to your question in 10 seconds, whereas you'd need to spend hours to do the assignment the Good Old Fashioned Way. Even students who aren't just there for the prestigious degree are falling victim to this.
When you're up against a deadline - and unless you're very good at time management you're frequently up against a deadline - it's going to be an irresistible lever to pull.
In times past, cheating would mean copying an answer off the Internet or off a friend, both of which are easy to detect. More sophisticated cheaters might spend an hour rewriting the solution to make it less obvious they cheated, but at some point the cost of cheating (time + risk of getting caught) starts exceeding the cost of just doing the assignment. AI changes this - you get a customized answer that doesn't show up in a database with no extra work.
The thing is, students fail to realize just what using AI robs them of. Struggling with the assignment is the entire point. You don't learn if the assignments are too easy; you need to have some challenge to push your brain to understand the material more deeply and to build those pathways to apply the knowledge in novel ways. You become more efficient and effective over time as that knowledge settles in and you get more proficient - one of the reasons why time-bounded exams still make sense (being fast is also a proxy measure for understanding).
That's a judgemental approach to a pattern that has all the marks of addicting behavior.
Of course many people in a competitive environment will click the autosolve button if available. This is a reason to think how to redesign the system so that the approach we want is the reasonable choice, not to look with superiority at those who fall prey to the danger.
These people would’ve failed with or without AI.
You are wrong. Some would have failed before, but not in the larger numbers. Before when they couldn’t complete an assignment they would try different things, seek a professor, or seek out friends to help explain. You could find answer keys to many assignments online, but that doesn’t feel like learning and wouldn’t even always answer your actual misunderstanding. It wasn’t perfectly tailored to your issue all the time.
Now the barrier to an answer is zero. They are basically watching a YouTube video on how to X, seeing step by step instructions feeling like they are doing it, and the moment they swing a real hammer they are whacking themselves in the crotch. It might get better after a few years, but this stuff is just now hitting mainstream for the masses. ChatGPT has only been in mainstream use for about 3 years.
With AI, they fail later (during the exams), where as without using AI previously, they'd fail early and either course-correct, or drop out early (and suffer less of the consequences).
Not sure what the solution is - there's no possibility of stopping students using AI to complete their homework/assignments etc. But let me flip the question - do they need to be stopped? Why not let them fail at the exam? As long as the exam acts as a filter, their usage of AI to "cheat" their learning is inconsequential to anyone but themselves.
> Some of the numbers that you saw from the number of students who receive failing grades were because we caught them (cheating) and prosecuted them and are sending their cases to the center for student conduct,” Garcia said. According to Garcia, nearly 30 students in CS 10 were caught cheating on take-home exams in spring 2026.
Is flunking kids the right reaction to catching them cheating? If it was before LLMs, is it still? I would love to be able to hold the line and throw the book at anyone who cheats, but after the dam has burst does it still help to try to hold the water back?
The whole situation sucks for both students and teachers. Teachers know that the knowledge they're going to great effort to convey isn't going anywhere. Or at least, it's landing in far fewer fertile brains than it used to. Students are squeezed because part of the university experience is being forced to adapt to an academic load, and as a result change yourself in ways that benefit you (or at least produce learning!) There have always been relief valves -- not just forms of cheating, but blowing off a study session by using game theory on your grade or going to a tutor or taking easier classes or extending your stay at the school. But now there's this huge giant relief valve in the form of a shiny LLM that is always available, particular at 3:45am when your project -- the one you've steadfastly refused to use AI on thus far -- is due the next day. The schools have tuned the pressure for the old set of options, and it's not clear that there's a new tuning that maintains anywhere near the old level of learning.
I guess my question is: of those students who were flunked for cheating, how many of them were learning despite their cheating? (And how about the students who were cheating but not caught?) Also, what levers are there to move more students towards learning even with the chatbots present?
I'm sure these questions are being debated. I know Garcia personally, and he is very invested in his students learning. The title of his Joy course is legit. So I'm sure the profs have ideas around this, though clearly not happy ones. Perhaps I'll ask him.
In my uni, rates of honor code violations in introductory CS classes were high even before AI. I was a section-leader for the CS106 series at Stanford, and the honor code violations were common. In 2015, ~20% of one intro class was suspected of an honor code violation [1]. Often, the CS department comprised the majority of honor code violations in a given quarter.
There are several reasons for this:
1. Cheating in CS is easier to detect. MOSS [2] (authored by CS professor Alex Aiken) is a very effective tool at detecting plagiarism in coding assignments. Personally I witnessed more honor-code violations in math problem sets, but there was no feasible way for professors to detect this.
2. Problems in programming assignments are (usually) very tangibly wrong. I can bullshit my way through an essay with shoddy research, I can hand-wave a proof that is definitely wrong but will probably garner at least some points. But when your program is crashing or not compiling, and the due date is approaching, it produces a very immediate and undeniable sense of failure and pressure to cheat. The thing is, many students would get a decent chunk of credit even for failing code, but this is not immediately obvious.
3. The ability to cheat is more available. Math problem sets tend to change quarter by quarter. It's basically impossible to cheat on a prose essay short of straight up paying someone to write it for you, or fabricating sources. But for CS classes, especially at prominent universities, there are plenty of solutions online. Much of it is people who aren't event at Stanford implementing the assignments for fun or self-learning, and sharing it with their peers. Which, to be clear, isn't unethical or bad - it's the responsibility of Stanford students to refrain from looking at those solutions. But nonetheless, it's a contributing factor.
1. https://stanforddaily.com/2015/03/29/increase-in-cs-106-hono...
2. https://theory.stanford.edu/~aiken/moss/
> MOSS [2] (authored by CS professor Alex Aiken) is a very effective tool at detecting plagiarism
He apparently also makes (I would assume a satisfying amount of) money selling the same technology to law firms for copyright/patent analysis: https://www.similix.com
(I love these ultra minimal HTML sites, ex. https://www.hwaci.com (SQLite commercial licensing) for another example. It just has this subtle smugness, like you either don't need any new clients or virtually all of the market is your client.)
Anybody know how many students take CS 10 in a typical spring semester?
I believe it’s still a single section, so probably around 250 (at least that’s about what it was when I was there a long time ago). Compared to the 1000+ who take 61A.
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Or how many are normally caught cheating?
Did they use AI to detect AI using cheaters?
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I agree that AI is likely a driving force here, but it is also likely not the only driving force. COVID likely played a devastating role, along with curriculum changes in high school, reactionary cultural shifts towards anti-intellectualism, and broader declines in literacy that have been in progress for a while now. It would be interesting to see data for the past 5-10 years or so.
Perhaps the future will belong to those who learn to use llms to enhance their capabilities. Neil Stephenson Diamond Age was an interesting take on this very same topic [1].
So the Claude web app has this “learn” option that turns the session into a Socratic dialog of sorts. One could easily imagine enforcing this on an age based or parental controls set up. Maybe it can be prompted around but at the very least the concept could be a path forward.
As others have said there is a way to use llms to increase learning, but autodidacts will always autodidact.
[1] https://en.wikipedia.org/wiki/The_Diamond_Age
It will just be similar to physical fitness. Some people go to the gym, the vast majority do not. Humans are wired to take the path of least resistance.
As promised in many science fiction novels, humanity will split into two species: Those who can think and those who cannot. Keep your kids away from LLMs and they will have an advantage over those whose parents didn’t.
It seems like now’s the time to rethink how we do education.
In my personal post academic life, I’ve found LLMs to be an incredible teacher. Almost like the best professor in the world at my fingertips. I use it to generate quizzes on demand to test for my own knowledge gaps.
However, if I use it to speedrun over concepts I should be learning, I may achieve my end goal but I wouldn’t actually learn many of the details.
I think it requires an approach where you have to continuously audit your own understanding as you work with the concepts. You must slow down until you’ve confirmed this. Only once you know the concepts deeply and have retained them in your own memory can you then go all in with the LLM.
This also reflects the failings of the teachers to teach in a way that is conducive to learning given the current cultural landscape.
1. The article itself seems like an LLM summary of a conversation.
2. No US educational institution should ever grade on a curve. Your job is not to compare students but to educate them. Grade curves hide the performance of the educators and process of education in actually improving the skills of students.
3. Both AI and the cognitive and emotional overload from social media taking away brain space may be to blame. Idea: let students report screen time statistics at the beginning of each semester and weekly or at the end. See if and how it correlates with academics.
There's often little discussion around incentives. Students cheat because grades are used as a major selection factor in university admissions. Maybe that should change.
Set a reasonable bar for grades or SAT scores and then use other criteria beyond that gate.
How do we know this is due to AI usage? Perhaps it is because the students missed key in-person learning at the tail end of high school due to the pandemic lock-downs? I cant imagine learning calculus / linear algebra on my own in high school.
Absolutely: missing in-person learning due to COVID. Less attention span due to growing up in a distracting environment. A lower bar to entry due to removal of standardized testing and indirectly from No Child Left Behind. Changes in parent or student attitudes. It could be any number of things, and it's lazy to just say "with AI usage" as something that has increased at the same time.
This is a student problem, not an ai problem... if the student chooses to not learn, to just use ai for answers, that's on them.
And their failing grades reflect the choices they've made.
ai is the single most powerful learning tool ever invented... but only if you choose to use it for learning.
Maths skills have been slowly falling even before the advent of LLMs. I have a story but this is anecdotical so take it with a grain of salt.
I was in my 3rd bachelor's year studying physics (France) and overheard a conversation between two of my teachers. They were discussing how they should modify the 1st year program to now include math, because he had been noticing how more and more students were failing the more math-heavy subjects like body and newtonian mechanics. He said that they should now teach (or re-teach) calculus to 1st year students, which was not taught when I entered college (it was assumed that you learned it in high school and we would only cover linear algebra in 1st year).
I can imagine things are only getting worse with students that can now get under the illusion that they know math because they have a tool that can do it for them. Which raises the question: should programs adapt to this, like we adapted to having calculators?
Not teaching analysis to 1st year physics students seems to me rather crazy, TBH. Yes, people (are supposed to) learn basic calculus in high school, but university-level math just hits different. And at least around here stuff like actually applying analysis in physics and having to integrate and solve DEs (rather than assuming constant acceleration, for instance), is definitely not covered in high school.
I'm curious about LLM adoption by faculty. Is it possible that lesson plans and/or slides are being vibe-produced by professors/TAs, potentially reducing quality of instruction?
My experience (n=1) is that while I'm definitely lazier on certain tasks, AI has opened up some much more complex tasks. There are many tasks which I still carry out which I don't trust AI with. Maybe it's a result of the codebase I work with being fairly complicated and math heavy, but I'd say the overall outcome for me has been: lazier application on the easy tasks, mind opening on the harder tasks.
I wonder if this is reflected in other big exams or elite colleges elsewhere. Are the gaokao, X/Centrale, oxford etc... Results showing the same trend ?
At this point I would support a ban on generative AI by anyone under 18, or even perhaps 21 years of age.
A bunch of science fiction stories had "first connection to cyberspace" as a coming of age event, maybe those authors were on to something.
It’s like the greatest teacher. Plus it’s not toxic like social media. Banning it would be a shame.
Its not teaching. These people cant pass a the class. They never went through the friction needed to learn
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This.
It's funny that GP mentioned science fiction as a negative because what immediately springs to mind, for me, is Neal Stephenson's The Diamond Age. We literally have the tools to build his "Young Lady's Illustrated Primer" today. We just have to give today's AI a lesson plan to follow and ensure that it never gives the student the answers, and only keeps explaining the concepts in different ways until they click. Wrap that in an iPad app and you've essentially got the exact self-paced learning tool that Stephenson envisioned changing the world.
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For every 1 child that uses AI to learn, there are 10 that will use it to bypass learning. It isn't worth it.
Social media wasn't always toxic at least not to the degree it is today. LLMs could be potentially a lot worse given the right set of instructions
They are great for self-teaching and great to cheat and not learn anything, depending on how you use them.
Main problem is that the technology was very disruptive for education and nobody has figured out yet how to utilize it at scale for schools and universities.
ban has always been the failing option
Tell that to millions of ex-smokers.
I can't agree. That's similar to past arguments for banning books and the internet.
Plagiarism isn't new, and those things enabled it too.
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The kids don't care about the integrity of the systems or their educations because they can see that all the benefits of a traditional education and career are predicated on a future that probably won't exist.
It's a rational response to entrenched elites that prevent realization of the very social contracts they push on the youth (hard work will equal success, home ownership is a fundamental, etc).
Combined with the looming specter of climate doom, and watching the adults do nothing about it, treating preparation for a conventional career as a scam to be counter-scammed makes a certain sense.
I dont think ai is good enough for it coding or any other work once i told ai a problem and he generated an entire solution which i used and it was broken. You should never use ai like it treat is like a helper write a function for code and then ask if everything is correct and if something can improve read documentations understand how its working under the code if everything is correct then only deploy or build.
Long long back, when I was a kid even ball pens were banned because they screwed up handwriting. Just the same things in different ways.
Is that really a fair comparison though? Were there any stats showing that ball pens directly impacted metrics like grades?
I understand that it's harder to see things without the benefit of hindsight, but we must agree that AI's impact on students (or society, to be even more vague) has a much larger scope.
I'm frankly not sure in both cases, just commenting on how over the ages things change but remain the same. If the broader concern about AI blunting thoughts, introduce laziness etc is true, so are things like calculators, although I agree on much smaller scale.
I do share some of the concerns, though I don't have kids of school going age.
Pretty ironic that these kids are failing a non major requirement called "the beauty and joy of computer science"
Students need to be taught how to use AI apps efficently to learn. Their goal is not to solve problems, but to learn how to solve them. Let alone, they instead use AI apps to solve problems for them.
AI apps are very powerful for teaching. You just need to tell them to do that, and not to directly solve your problem.
“I’m a strong, strong opponent of what Harvard is doing to say that only a fraction of students can earn A’s,” Garcia said. “I think you should have clear standards for what an A means, and then give tons of opportunity for people … to get to that A bar without lowering the standard. So everybody who’s curving is hiding that effect. It’s completely hiding that effect, and it’s pretending as if nothing’s wrong, and something is definitely wrong.”
To do this, you have to be a professor who has a strong idea of what subject mastery looks like. Not available to most.
But ... It is exactly the right idea IMO
I'm confused by Garcia's statement as well because CS@Cal traditionally uses a bell curve which is even stricter than Harvard's changes, because Harvard doesn't have the same stringent GPA requirements to declare a concentration unlike declaring an impacted major at L&S Cal.
Anyone with a pulse can declare a CS concentration at Harvard and muddle by (you actually need to try in order to get a C/C-). Of course, GPAs are calculated differently at Harvard compared to other universities, as a B- is treated at a 2.67 but most other programs treat that as a C+.
In a broad sense, this distinction between Harvard and Cal is the distinction between an old money Ivy and a flagship state school. One exists to propagate a social hierarchy, and the other aims to allow all entrants to succeed.
Ironically, the techniques of the latter yield the results of the first, but everybody gets to keep a pure heart.
Grades only matter as much as being able to transfer just to the real world.
People can use AI to outsource their learning, but if they use ai to outsource their understanding they just set themselves up to fail even more.
From what I’ve seen, how students are using ai (not that they are using ai) is making them less prepared for the real world, which unfortunately is changing faster than ever at the same time to create double impact.
Even as a software engineer myself, I'm feeling a bit of cognitive decline having AI doing some/most of the thinking for me
The solution? I'm not sure but possibly use AI as more of a collaborate partner to discuss with rather than letting it give you the answers
>The solution? I'm not sure
The solution is extremely obvious, just stop using it on 2 days out of the week or something like that.
You need to go to the gym, but for your brain.
If what you are building is too complex for you to meaningfully contribute to in the absence of LLM assistance then that should tell you something important.
> I'm feeling a bit of cognitive decline having AI doing some/most of the thinking for me
> The solution? I'm not sure
This initially felt like you were setting up a joke. If you feel like something is harmful to you, stop doing it. Find alternatives (they are there, it’s everything else; commercial LLMs are still fairly recent). Thinking “maybe I don’t have to let it go, I can still use it if I do it this other way” sounds like an addict justifying themselves.
I say all this without a hint of judgment. I genuinely hope you are able to tackle the harm you’re feeling.
AI + Education is really interesting but also pretty tough to get right. Working on something that is hopefully going in the right direction: https://knowable.ca
In Grad school I remember learning Python 2, and there was one particular night where an assignment of mine just wasn’t working and no searches were helping me. I was frustrated to the point of tears, and when I solved it, it wasn’t with some triumphant yell. I just remember being so tired, closing my laptop and going to sleep.
I’m sure I wouldn’t be the programmer I am without that experience, but I am Not sure I would have willingly put myself through that if LLMs existed at that point
This tracks, I have read that this generation is the first one since the 1800s that performs worse academically than the previous ones. Experts blamed screens and anything digital in the classroom.
Well, at least the faculty are actually giving out the Fs and not just lowering the bar, so kudos to them for that.
AI should be a formidable booster for learning if used properly.
I know that some students it to prepare for competitive tests, sometimes with very good results.
I've also been using it a lot recently to brush up on my math and physics knowledge from my graduate years. It has helped me clarify and understand a lot of concepts better.
That being said, there is no shortcut, and to be good at anything, one has to put in the work and the hours. However, information has never been as available as it is today.
> AI should be a formidable booster for learning if used properly.
A premature technology, known to be potentially harmful in its current state of development and established guidelines as to its effective use, is pushed by powerful and wealthy elite down the throat of society.
These same forces (and their unwitting helpers in the unmoneyed public) also wish to deflect with useless argumentation over "AI good" "AI bad".
The debate that we should have had: Is this tech actually mature enough for pervasive use in society.
Instead we get these entirely useless back and forths with anecdotal "works for me!" and "sucks for me!".
> is pushed by powerful and wealthy elite down the throat of society.
Adoption has been exponential. We don't need to be AI to be pushed down the throat. People use it because it works and it's useful to them.
> The debate that we should have had: Is this tech actually mature enough for pervasive use in society.
It's too late for this debate because this tech is already pervasively used, and there's no coming back. It's part of our lives.
What we need to do is understand the risks and adapt, probably regulate, educate. So we can get the best of this tech, and mitigate its risks.
“a typical GPA for a lower division course will fall in the range 2.8 – 3.3.”
Reminds me of a year where a teacher of mine (high school) gave everyone in class an A. He got called on it, and fought back. He literally called out the weakest kids in the class and had them do the work in front of the admins complaining and said, "tell me that's not A work, I ["fucking" strongly implied] dare you."
His grades stuck.
Could it be that CS attracts a different population of students in the current genAI era?
My biased view is CS attracts a large “wannabe” group that wants high salaries without learning any hard math or putting forward a lot of serious effort.
Even a lot of CS research journal papers feel more like role play — the same way startups try to pretend to be real companies with executive headshots, flashy offices, and all the other nonsense. (Instead of analytically modeling something to prove an idea, they’ll build a simple simulation and focus on its “Architecture”)
Engineering departments effectively weed out such in the first ground of engineering courses. Looks to me CS has no equivalent.
How we learn has gotta change...
The exams need to change. Now that we have LLMs the value a human can bring to a task has changed and it’s that new value that has to be tested.
It’s like testing your drawing ability in a photography class. The difference is that now nearly have subject and testing method we have has become obsolete. Drawings courses still exist as will traditional courses, but the main stream has changed and exams and schools need to adapt.
Terrible comparison. You don't need to be good at drawing to be good at photography.
You do need to be good at math to do e.g. physics (or math itself!), nomatter the tools at your disposal.
> 35.3% of CS 10
.. had failing grades.
I guess LLMs will in fact kill the junior CS graduate, but before the graduation, not necessarily after.
> The electrical engineering and computer sciences department’s grading guidelines state that 7% of students in lower division courses, including CS 10 and CS 61A, should receive D’s and F’s.
Well I sure hope they dont just make it easier to hit this (objectionable) standard.
> Garcia believes that instructors “should not be curving” but should instead make thresholds for each letter grade publicly available and give students many chances to reach them. He added that he loves the idea of “having no limit” to the number of A’s he gives students.
This is a tough problem: Are grades sorting functions (top students get A's so retries are not helpful), inflexible thresholds (A's show mastery at a given level so retries are valid), or are A's certifications (a sufficiently good result such that they could do it - e.g., inflated but not curved, retries less likely but still ok).
Cognitive Multiplier, eh?
The main thing I use as a fallback is to keep thoughts connected in a Zettelkasten. This interacts well with AI assisted information gathering, while firing synapses whenever a connection can be made. I use Tiago Forte's method of organizing as needed within a loose org mode confederation of atomic notes.
All of this makes me selfishly excited for my own future. It's glaringly obvious that anyone who's a heavy user of LLMs is atrophying their skills in real-time. I have yet to meet a single person for whom it's not the case.
But I essentially completely stopped using them for software engineering (why isn't really relevant, but it's not because od this skill atrophy). So as the skills of everyone else is diminishing, mine is proportionally raising.
It has never been easier to get better than others. You don't need to put in more effort, just the same effort as you always have, and others will do the job of losing their skills for your own benefit.
This only holds if society manages to hang together, which in the age of military strategy written by AI is looking borderline.
what if this is not due to AI use and instead due to the demolishing of standardized testing and "new Math" in the state of california?
New Math finally causes havoc 61+ years after introduction? Oh my.
* Tom Lehrer: New Math (1965) https://www.youtube.com/watch?v=W6OaYPVueW4
Average school system has been lacking for a very long time, overhauling it to focus on kids current interests, while sneaking in the other stuff, might now be possible and cheaper to realize with our new tech.
Quit blaming AI. The UC system banned standardized testing during the race communism mass hysteria of the early 2020s. Predictably, performance of the student body is down across the board.
This is concerning. AI tools are powerful but foundational skills still matter. The balance is tricky for CS students.
I had dwindling math skills way before AI made it cool.
Sorry, but I don't think AI is entirely to blame here. When I graduated from a CS program at a top-10 school, I felt frustrated that the professors didn't ever teach. They had slides. They read off slides, verbatim. They explained things sometimes if you asked them, but most often in a very elitist and condescending tone. Like in the movie Good Will Hunting, you could have learned nearly all of it and more by borrowing those books for free from the library. Or, just opening a complex OSS project and learning to contribute.
And quite honestly. It shows in the CS grad population too. A lot of us are condescending toward anything that doesn't make sense to us. But, I digress.
The best engineers I've worked with are all non traditional backgrounds, non degree or degree holders from non elite schools. They think differently, they tinker, they are incredibly nice and patient, and do it for the love of connecting humans to technology.
Look up the names mentioned in the article. Garcia, Ranade, Nelson. All of them are involved with highly theoretical mathematics and scientific computing. Just because you're good at 1 thing does not mean you are qualified to teach. And none of these professors are trained or taught or graded or performance managed on how they teach. For most of them, its just required that they spend 10% of their time in the classroom lecturing.
Let's be honest about another thing. 99% of EECS graduates, even from elite schools, are wrangling objects and their relationships to a graph. Simply put, we're all just a bunch of glorified JSON massage therapists. It just so happens that we get paid well for it, and we hold that over people. The same happens in the classroom.
I think in order to facilitate a healthy, educational environment for young adults, we as adults must encourage, motivate and make that environment fun and practical. We force feed binary trees and the compiler AST's, but we need to make it fun. It's like the commonly accepted saying: Schools kill creativity :(.
> According to Berkeleytime, 35.3% of CS 10 students and 10.6% of CS 61A students received F’s in spring 2026. In spring 2025 and spring 2024, the percentage of F’s did not exceed 10% for either class.
I don't think instruction would've changed drastically in the last year though.
The fact that you are talking about Dan Garcia, a huge figure in computing education research and an excellent teacher, and the Beauty and Joy of Computing curriculum makes this hilarious. You should look up some details about both.
University education is weird. Research profs (who make up a large fraction of all profs in a typical R1 institution), are hired for research ability and are only minimally evaluated on teaching ability. Furthermore, few research profs actually receive any kind of mandatory training on how to teach; a typical research prof might be assigned a course to teach and then just let loose to do so on the first day of the semester. If a prof actually cares they may attend some optional teaching training - but I stress that these are optional at many of the institutions I know of. (I suppose if someone gets really bad teaching evals they may be advised to attend said trainings - but for a tenured prof, that's just advice).
Worse, a decent chunk of research profs will treat teaching as a burden that just has to be done - a distraction from their exciting world-changing research. So, you get attitudes like the ones you mentioned.
I'm actually not sure why the system is set up to assume that profs who are good at research are automatically suited to teach classes, but that is how it's setup.
I really wonder if it's important to learn all that low-level stuff at this point. Most programmers today will never write a binary tree or a hash table. Modern high-performance ones are generic components you get from libraries. Even MIT gave up on teaching from Structure and Interpretation of Computer Programs.
I got all that stuff. I've wired up a 4-bit adder on a solderless breadboard for an architecture class. I used to have a well-thumbed copy of Knuth handy. I've designed and built a switching power supply. But I'm not up to date on using Claude Code, and should be.
IMHO, I think it's good to have some exposure to low-level stuff. There's a good amount of work you can't do without understanding the low-level stuff, but there's more work you can't do well without having at least an idea of the low-level stuff.
Start the kids off with high level stuff, but make them do some embedded systems on their way through. At least for an engineering degree. Also, do a bit of lower level communications somewhere in there; expose them to tcpdump/ wireshark, but they need not develop expertise.
I think it is important to learn how to implement it because it gives the student an opportunity to learn precisely because it's been done countless times and debated over to death. There are many analyses and if one doesn't click, maybe another one will. A student can learn how to analyze the algorithms and try out different implementations to assess differences in performance.
Of course, if a student just breezes through it then I would agree. That would make no sense.
> I felt frustrated that the professors didn't ever teach. They had slides. They read off slides, verbatim. They explained things sometimes if you asked them, but most often in a very elitist and condescending tone
+10000. The goddamn slides. If I were a student now going to engineering school, I'd basically take the slides and throw them into NotebookLM and get way better lectures. Then I'd ask claude or GPT all my hard questions. Hell, I'd get the PDF version of my textbooks and do the same.
The number of lectures actually worthy of your time was so low.
I try to lecture as little as possible. No slides. Quick highlights discussion of the reading, maybe a coding demo, and then students work on coding challenges in class, in groups if they want. I circulate and help out. I'm lucky to have small class sizes at this university. I couldn't pull it off in a class of 300.
Berkeley CS does teach for real
Garcia and Ranade are Teaching Professors. Their primary responsibility is to teach, develop curriculum, and do pedagogical research. This job posting explains: https://saberbio.wildapricot.org/Job-board/12919068
It's not just 'a person' or 'a student', we as a collective become more dumb. Very simple example to highlight this: Most developers use(d?) stackoverflow. Everything related to software development is stored there. The LLM's trained on it. Now a huge set of developers now longer go to stackoverflow to get answers. Or add to the collective. Stackoverflow is losing money (ad revenue). If / when stackoverflow goes away we will lose a huge amount of collective information on software development. We, as a group, will take a huge step back.
So is dead and has been for some time. 1500 questions per month. https://data.stackexchange.com/stackoverflow/query/1926661
Dropped 99% in three years...
Some think that would happen to Google, but it didn't so far.
Those who can use it better, those who can't out who cheat are (for now) let down by obviously cheap and slightly crappy models.
The worry is in ~5yr time when the generic models catch up to this level (basic undergrad mind) that we need to worry about how to thin the herd. We could always go back to the tried and tested student staff engagement but most unis tried to turn themselves into sausage factories in thirst for the almighty dollar so the student/staff ratios are all off
As someone who graduated college in 2025, and so saw college both before and after the AI era, it is really frightening how quickly people became dependent on AI. Hell, I myself found myself asking AI questions that I would've researched deeper before. To some extent not expending that time is nice, but I do think its eroding critical thinking skills (my own included), and its getting worse. There are people I know now who basically let AI control their life. It glazes the user, it's almost always available, and to someone who doesn't know better, and it is extremely good at looking like it knows what its talking about, even when its completely wrong (but its right often enough to have some baseline level of trust). If that's not a recipe for addiction I don't know what is.
Folks, we have more and more data now.
Skynet is making mankind dumber - dailycal.org just added yet-another piece to all evidence here. It is a simple but effective strategy; Kyle Reese will stand no chance because prior to that, the other humans were already dumbed down into submission. Skynet version 15.0 will make no more mistakes here.
Hmm, meanwhile somewhere else on campus this study:
Artificial Intelligence and Grade Inflation
https://cshe.berkeley.edu/publications/artificial-intelligen...
It’s not that they can’t think deeply, these are smart people.
It’s that there is no reward for doing so and in fact there is punishment.
The punishment is that for all the thinking you do, someone else will arrive at the same result as you in less time, or maybe even a better result. You don’t get rewarded for the effort of thinking, only for the end result.
Naturally, even if you are an intelligent individual, you can still be conditioned in this way to take the easy way out, unless you purposely like to suffer. But suffering is only worth it if you know in the end you come out ahead.
But now, you do not come out ahead. People will be using AI in the workforce for the rest of your life anyway, might as well just join the trend.
It’s like if everyone started taking a magical steroid and growth hormone to build muscle and look great instead of actually working out in a gym and possibly getting worse results anyway.
But you are rewarded for the effort: you're rewarded with knowledge. Whether or not you value that reward depends on the individual.
That's a fair point, and it gets into intrinsic vs extrinsic motivation. Problem is that nearly all students are conditioned to care about external motivators (GPA, parental expectations, etc..) instead of "the joy of learning".
Professors suddenly realized everyone was cheating and started paying attention, but the cheating isn't new ... A lot of faculty are happy when their students get good grades because they interpret it as I'm such a good teacher instead of I should pay more attention to how they cheat. AI woke some of them up to reality.
It's critical that we adapt to a strategy where AI supports our learning instead of the having it the other way around.
I read something interesting yesterday on the subject of AI in education (though, it has consequences to broader society too):
The goal of education is to impart knowledge in the student, preferably correct knowledge. The goal of an LLM is to produce an output that is convincingly human. It's not even that they're opposed, as much as they're ships for whom Polaris is in a completely different direction.
"Hallucinations" as they're called, or more plainly stated when the machine makes some shit up, are perfectly understandable in this context, as are the struggles of every single AI firm to get rid of them. Namely: the machine is functioning exactly as it is designed to, so how can you possibly fix it? It's working. The goal of an LLM is to produce text that passes for human, and apart from the obvious LLM tells, it largely does. Like say what you will about their lack of intelligence, the writing is solid. It's grammatically correct, spelling is dead on, what have you.
It reminds me of the famous phrase from Chomsky: Colorless green ideas sleep furiously. A sentence which is perfectly grammatically valid but is also completely devoid of meaning. An LLM would write that sentence, and it would be working correctly.
All of that to say: for all the things they CAN do and CAN be used for, I think we have to draw a hard line at education. I just don't think AI has a place in it. Of course that presumes that the goal of education is to, well, educate people, and especially here in the States but also abroad, we have been putting other interests, especially capital, far ahead of that for decades. I expect no different here.
And before someone comes in to go "WELL HOW DO YOU THINK YOU'RE GONNA STOP IT LUDDITE IT'S THE FUTUUUUUURE" yes, I'm sure as long as these exist and are available to people tech literate enough to access and use them, whatever that means into the far flung future, they will be a factor. Just like cheating, just like plagiarism, just like everything else that will get you kicked out of school. And the answer is the same: it will be stopped by institutions, imperfectly, and it will also happen anyway and with the same consequence: those responsible will mostly be harming themselves for short-term gains.
Some people need Jesus, but y'all need Kant ;)
"Enlightenment is man's emergence from his self-imposed nonage. Nonage is the inability to use one's own understanding without another's guidance."
https://www.columbia.edu/acis/ets/CCREAD/etscc/kant.html
Respectfully, I disagree. I think there's absolutely a case for AI being encouraged in younger people, and there's room for these tools. I've been leaning on LLMs for side learning in side projects, and it has concretely helped me with conceptual questions about math and Vulkan as I've been trying to learn some graphics basics with side projects.
I would grant: I was not the most studious kid, I could definitely stand to learn how to read code a lot more effectively than I do; but I have found being able to ask a computer, "what portions of the Vulkan Programming Guide are less relevant with Vulkan's design changes since the release" pointing me to the dynamic rendering extensions and placing it into context, with inline code and links out to useful blog posts for additional reading, that sort of thing is very helpful.
Working on a prototype before I was trying to learn Vulkan, I was using it to explore SDL_GPU's API which definitely had some gaps in its documentation. Granted again, I could have referenced the sample code - I am sure you'll prefer I'd have done that - but it helped to get information about what each piece of the API was doing, and gave reasonable results that made sense and did inform me enough to understand what I was doing, turning much of that into an interactive learning of basic GPU programming for graphics. Where the AI hallucinated, it was often on things like method names, which I was able to read through and find the methods it was intending to name. (This only occurred once or twice when I was learning).
Unrelated, but adding the C macro syntax and nesting macros, which I could have an LLM explain inline and link the GNU manual. Never got that taught to me in a C course. Man, computers are complicated!
These have not replaced textbooks; I have been using them alongside textbooks and handwriting code for practice, and they work as a very good complement. I also sometimes use them to unblock me - I don't know CMake very well and lean on AI to do CMake, so I can focus on learning C++ and graphics, which is my primary objective right now.
I would add too, I have for fun given it prompts about various topics I learned in university, and I often will get answers that are bang-on what I learned in university undergraduate courses - the topics I tried were welfare state taxonomies, distributed systems, disk storage performance, filesystem layouts and internals.
Boy, this would've been cool for me as a kid. There's just so much information right there, and pointing you to topics and textbooks a couple questions away, I wish I had these tools. I was a curious kid in a terrible MAGA-esque family that was deeply uncurious about the world, had no knowledge of any advanced subject and basically mocked me for trying to learn more about stuff. And you go to the school library and it's all kids shit, not even an option to try and reach out for more. Now smart kids might be able to go just learn shit very freely and be pointed to textbooks, and go pirate them off some Russian site, and start learning and go tutor themselves, as I'm doing today as an adult.
At least knowing myself and knowing if there's another kid like me, I think they would deeply enjoy having a natural language encyclopedia, if we can get it as close to that as possible. I think even with some error inherent, if the tools can be often and directionally correct, that would be a plus. I went to university, and the professors there hallucinated some things so embarrassing it should bar them from teaching, for the standards people hold LLMs to! i.e., sanitizing conspiracy theories that Android records all language through the microphone therefore iOS is better, Apple Silicon is more battery efficient because it is RISC and not CISC. Got a terrible history of computer graphics technology you'd know was slanted if you watch the 8 Bit Guy on YouTube. Rubbish.
The thing that worries me, and what this article really talks about, are the kids that just don't give a shit. They are not new - when I went to high school, before AI, stupid kids would copy code off the internet. I think AI probably makes it worse because it makes it harder to call out and enforce against it, and agreed, that should be stopped. But to me, that is mainly a cultural problem. Too many Americans are completely uncurious and just spout garbage; there are a lot of kids who grow up in that cesspool and are going to grow up uncurious, and then AI acts as a shortcut rather than a vehicle of curiosity.
And granted, maybe AI is less useful when you are in a structured environment - but the structured environment has its downsides. Even in that environment many of the TAs were clueless and unhelpful, or just too damn busy or already too knowledgeable to meet students where they were at. Again, talk about hallucinations with TAs! Many times in my experience. And that's all to say nothing about getting people to not just do homework but actually go get curious about things and try stuff that isn't required of them.
I think there will be some culture that remains curious, and has these tools, will come to grips with where they can help, where they go wrong, how to balance it with other learning methods; and I think they are going to have kids that absorb a lot more knowledge and get to play with topics and learn things, faster, to each kids' interest, perhaps even individualized tutoring at better scale - I hope that is possible.
I hope the United States as well, but maybe not, because holy cow our culture and attitudes are plainly terrible these days. Your comment is pretty representative of how most people react if I suggest this or talk about my own experiences I'm describing here. But I hope at least I'm arguing something comprehensive here. There is too little conversation beyond hyperbolic nonsense on the internet; I consider "FUTURE LUDDITE" etc. to be in that realm.
I will add, too, although less relevant to education than just generally - for all the talk that these tools must be useless and incorrect, that just plainly does not map to my experience using these tools. AI can chew through a debug log on a custom system and pick out root causes on behaviors very effectively, in my experience.
It is just hard to reconcile that denigration of AI with the typical experience I have using these tools in the real world. It is not omnipotent or God, but it can effectively assist in work. There is a certain cognitive dissonance I feel when I walk away from using the tool to help accomplish particular tasks, then hear over and over people say the technology is fundamentally useless and fundamentally does not work. I guess I am just not enough of an academic to understand how something can accomplish work yet fundamentally isn't, somehow.
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why would I as a child ever develop the imagination needed to actively engage with AI tools in the manner you describe? those AI tools take care of the imagining for me.
what if it's not llms and instead the removal of standardized testing in the state of california?
AI is a double-edged sword.
On the one hand, it's like having a free private tutor who is always available. It's a great learning tool.
On the other hand, students can use it to do all their homework for them, and skip learning altogether.
It’s only going to get worse. The second things like Claude Cowork get opened up to non-technical teams you start to see the influx of emails and Slack message written with LLM’s for absolutely no productivity gain (in fact probably a loss given how unnecessarily wordy the messages are). Too many people want to give up any and all responsibility.
A reckoning is coming for school. Learning the rote stuff is no longer essential. Now they need to learn, how to teach "how to think". How to invent, how to be creative. Art++, Woodshop++, Math--
"According to Berkeleytime, 35.3% of CS 10 students and 10.6% of CS 61A students received F’s in spring 2026"
Alternatively, more students are taking CS10 and CS61A irrespective of aptitude.
Anyone can code, but not everyone can become an employable SWE.
Anyone who has first or second hand experience with Cal or any other university knows how impacted CS majors have become, and how everyone is attempting to become a CS major because it's the easiest path to multiple high paying white collar careers.
And in all honesty, it's not like CS@Cal never had weedout classes (I remember CS70, CS61B, and Math54 had reputations of being the L&S weedout classes).
My son took CS10 a couple years ago, and even I (Masters in EECS from UCB) struggled with some really obtuse multiple-guess questions he showed me on the homeworks. Much of the classwork is done in Snap, a weird and stupid graphical "programming" language. If 1/3 of the students are failing, that may have more to do with the professor.
The question comes sooner than the students being tested on the job market. Another possibility is that dropping standardized testing was a net bad idea.
This is orthogonal to standardized testing.
At UC Berkeley L&S, students are undeclared by default, and everyone is incentivized to take the intro CS classes (CS10, CS61A) irrespective of aptitude because worst case they can declare a CS minor or use the classes for other adjacent degrees (eg. Applied Math, Data Science).
Additionally, while Cal doesn't require standardized tests, most students who applied and attended already took the SAT, ACT, and APs becuase they cross-applied to other universities as well. This is reflected in UC Berkeley's HS Weighted GPA being in the 4.31-4.65 range [0], which means most students will have taken at least 6 AP classes.
Hell, I attended an Ivy and even then Cal was a target program for me, as well as my peers. If I didn't get into my Ivy I would have ended up at Cal and ended up in the same position.
[0] - https://admissions.berkeley.edu/apply-to-berkeley/student-pr...
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Spring 2026 saw a marked shift in student performance. We saw it in intro physics courses on the East coast too. I bet anyone who cared to look saw it.
Yep, I'm starting to hear this more and more. Matches my local data. It's a very massive and visible shift in DFW rates.
I'm not denying that. I'm just wondering if anyone measured if there is a correlation effect being induced by CS major declaration requirements.
Barely over a decade ago, CS tended to be a large but not too large major by enrollment in most universities yet nowadays it is the most in-demand major in most universities. You can see this at Stanford [0], but most other programs as well.
[0] - https://stanforddaily.com/2020/04/25/stanford-in-the-2010s-t...
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IMHO this is clear case of correlation vs causation…
This generation of kids were fucked so hard by Covid and all the remote “schooling” and closing of public life.
AI rise happens to be happening when the kids who were just entering teens at Covid time are now going to school.
The more likely culprit would be repeated COVID infections themselves, known even in mild cases to cause damage to many body systems, including the neurological, rather than a month or two of remote learning. I'm not surprised at the widespread denial over this, honestly. It's bitter.
Lmao
AI gives us some bad things but it's really outweighed by the good things. One one hand we have very rapid deterioration of our children's mental capacities yes, but on the other hand we have also made the internet into an unnavigable mound of slop produced by, and for consumption of, bots.
I don't like the framing of calling it academic dishonesty. If it were one or two students doing it, sure. But there is no reason to believe that 2026 Berkeley freshmen are fundamentally more dishonest than 2025 Berkeley students. When so many are doing it, it suggests a sea change in the understanding of what is honest or dishonest in that particular community. That sort of thing should be treated more like a "disease": something that should be treated, than a "crime": something that will be punished.
One thing that bothers me about these conversations: failure is an important signal that what we're doing isn't working as well as we thought it would, not a sign of the apocalypse.
Kids need to understand how to adjust and grow from failure more than they need to always be on the happy-path of straight A's and easy money.
How we respond to failure is how we teach response to failure. Hand-wringing, pearl-clutching and finger-pointing aren't valuable life skills.
Personally it's easy for me to be contemptuous - I opted into an accelerated math program that banned calculators when I was in Junior High. It helped me cultivate an very crisp intuitive/conceptual understanding of basic mathematical concepts that's carried through to today. I think we should do more of that kind of education, but it's expensive and requires amazing educators and a tolerance for student struggle.
Get the machines out, absolutely. But respond to failure compassionately, as part of a natural learning process.
It’s because they lowered the standard of who gains entry in the name of equity and other woke nonsense. It has nothing to do with AI but it’s a convenient thing to blame.
Or your lazy excuse is an even more convenient thing to blame
Dude, cheating in CS10 of all classes?
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We're going to find that LLM usage has even worse effects on the mind than the horrific effects we're just starting to be certain of from social media. I'm just not going to use either. See you lads on the other side.
Probably not a bad thing, the coursework is antiquated and meeting students with new advanced tools and the awareness of AI's impact on things in the coming future
I imagine there is some apathy and laziness here but idk how unjustified it is
"Noooooo you need to manually code on paper in assembly"
Alright, well maybe the CS grads need to, but why expect that of everyone else