> 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".

> 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.

> 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. Perhaps this is rather a sign that you currently shouldn't jump on the LLM hype train, but rather attempt to get a good foundation on the basics. When the whole LLM area becomes much more "stabilized" (I see signs that this is currently happening, if only for the reason that training state of the art models has become more and more expensive), you can still get into LLMs if you want.

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.

This is the right balance for me as well. I use an agent to generate a first-pass attempt, and then (deadlines willing), I manually read every line at least once so I understand what the code actually does. Then I manually fix the inevitable slop that is mixed in with the good stuff, and only once the code is up to my personal standards do I send it. This probably reduces my “AI performance boost” to 30-50% instead of the huge gains reported by others. But I retain the ability to reason about the codebase and use AI much more precisely when I’m trying to troubleshoot production outages or subtle bugs — something I notice the rest of my team struggles with, since adopting “agentic workflows” everywhere. I think actively working to retain some cognitive flexibility and “muscle memory” around coding tasks is going to be rather advantageous in the long run.

Same, but also because it feels like it takes longer for an LLM to do it. I think that's something people who are into gathering personal metrics should do - measure how long it takes to type a prompt / have the LLM fix things vs just doing it yourself.

LLMs are making me smarter. I have more code to read!

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.

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.

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.

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 also had exercises for which the solutions were given, and we didn't reach for them immediately...

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.

In my own experience, the only path I truly gain intellectual benefits is the one where I work closely with the LLM, test very narrow hypotheses, and leverage it for learning over producing. Trying 5N paths is useful and sometimes yields interesting insights I’ll retain, but it’s not the rich, challenging, deeply engaging kind of process I find I need in order to develop useful knowledge and skills. So yes it’s an accelerant for people who want stuff from me, but that doesn’t map directly to learning and building skills. I think that mismatching is really important.

To help learn I use LLMs to generate practice exams for whatever I'm trying to learn, then on the questions I struggle with have the LLMs explain the logic and point out my mistakes. I haven't been in college for over a decade, this is just for topics I'm curious about and want to learn. For any serious topic I recommend auditing the practice exams with a different LLM than the one used to generate to help reduce hallucinations. Seems to work well for me. I quite like reading the "thought" processes shown by DeepSeek.

Understanding in what ways it can be useful and in what ways it can be counterproductive in long run requires a certain degree of experience itself.

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.

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.

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 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.)

The jump is very likely due to AI usage and lack of skills in mathematics. It seems like prerequisite classes are not being fulfilled. "Ranade said students are expected to enter the course having taken classes on linear algebra, vector calculus and mathematical proofs. However, she found out in office hours that many students struggled with linear algebra, and was even more shocked when one student told her the linear algebra class they took at UC Berkeley had an “open-internet, open-AI policy” for homework and exams."

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%).

> 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.

> Yes, if they are "responsible" for the code delivered, where responsible means they understand the code, the architecture, the decisions made, etc. This is a good principle to maintain, I think. I'm not a professor, but I manage a team of about a dozen people. The maxim I have is: "You're responsible for anything that hits git." Don't care if the LLM generated it, or the LLM told you if it's a good idea. If you commit it, you are endorsing it as a good idea - so you're the one I'm going to ask about it. I see the same principle at work in your pedagogy. > 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. This hits. Especially this part: > and really, what could I do if I were? My completely unsolicited opinion: you're doing a responsible thing by teaching these students how to use AI as a reference, and keeping them honest about not using it as a substitute for their own critical thinking.

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.

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.

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.

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.

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.

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

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.

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

It depends how you use it. You can either get it to explain a concept, or do your homework for you. Its a bit like the decision students have to make as to whether to review their material before exams or go out partying. Overall it just seems like a huge waste of money to piss away the huge tuition cost your parents probably paid.

You can use an llm to get out of doing homework but you can also use it to ask every question you would ever wanted in a 1-1 tutoring session. The problem is kids will use it to cheat on their homework. If we can’t deal with that problem then a ban is necessary. But these things can be phenomenal teachers if you use them properly.

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.

And how do you propose that to work if the internet is still full of AI services that just give you the answer or write your essay? The only way an Illustrated Primer can work if you can’t trivially cheat. Which is to say, it solves nothing compared to the current situation.

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.

It's critical that we adapt to a strategy where AI supports our learning instead of the having it the other way around.

> 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.

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.

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.

In my experience, AI seems like it’s helping debug problems, but it’s very hard to tell when fictional information starts being added. I’ve wasted a lot of time trying AI suggested solutions that I only realized were pointless when I started asking questions like ”I think this distro is missing a package, could that be the problem?” It would agree and tell me a specific package to download. I’d then ask “could iptables be the problem?” It would agree and give me a specific configuration to change. LLMs can be useful, but I haven’t found a way to use them where I’d be confident in using it to solve technical problems I didn’t already deeply understand.