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 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.
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.
The funny thing is, maybe not noticing one can be the actual sign of it :)
And I'm just afraid this is what cognitive decline feels like from inside the deteriorating mind.
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.
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.
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?
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.
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.
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.
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.
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.
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 had the experience to keep calling out AI to simplify and downgrade the solution to something primitive, which ended up smaller, faster, easier to maintain. Juniors with real world experience would not bother, they’ll take the first working AI result.
I hope this isn’t the case. It is the route I took, but it also doesn’t seem to be a likely route going forward. Strong CS grounding is feasible for sure, but I have a hard time believing that a meaningful number of people will be spending the requisite years coding manually.
I disagree, the definers of taste; art and food critics, movie and book reviewers, don’t need to have learned the craft by doing. Taste is a separate skill.
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.
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.
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.
> 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.
What you said there is just an extension of the elimination of friction that the silicon valley has been pursuing for the last 15+ years.
But that is just.. well. Their business model. Not a force of nature.
But now we delegate thinking itself, so I wonder what is left.
A proper nights sleep is massive! I'd put 99% down to this..
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.
The same goes for speed and quantity of input, as to what the human is designed for (not literally designed). Be it social media with it's infinite scrolling, cars racing by as opposed to looking out the window a few times per hour because you see someone/something, constant sound input if you live anywhere remotely busy or work in a busy office.
The point I'm trying to make is that the world used to be comprehensible for the human. Some understood a little complexer things, some only the simpler things. Now there is an overload of everything. So, most humans are in survival mode wether they know it or not. Hence the many seekin mindfullness etc
No matter, it's an observation, not a judgement or opinion on it. The world will just keep rushing forward. Some have a slight hand in the direction it goes for better (never) or for worse, but spiral it will.
>> The world will just keep rushing forward. Some have a slight hand in the direction it goes for better (never) or for worse, but spiral it will.
The systems are too large and self-propulsing for anyone to really control. Consider the rainforest. How many millions of variables interact, nobody is in charge, everything influences everything in a billion different ways. You might say, well we can cut it down, so kind we can control it. Allright, let's continue to spiral. You might build a city there after a few years. Still in charge right. But it get's too hot because there's no vegitation, so you have to change again. And then we find that people keep getting strangely sick, and scientists find some special mushroom that survived and apparantly thrives on the mix of cut trees and diesel fumes and their spores in the air are poisonous. I made that up, but you get the idea hopefully.
I still did well, but I had gaps for which there was no help outside of the internet available.
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.
I've heard LLMs can be helpful in limited targeted ways. But not as some kind of "game changing" accelerant.
The downvotes are just a sign of the times. It's also something to observe and think about..
Other fields may be different. YMMV
Asking suggesting or arguing to go deeper is impossible. There is a new path of least resistance and it saddens me.
Sorry, but I highly doubt that. Has a very "old man yells at clouds" vibe.
But apparently some of the smartest people in the world have lost the skill? But the commenter haven't, because why, they're 15 years older and thus immune to the same LLM-effects?
Plus, the issue with people having trouble sitting still for 30 minutes precede LLMs with decades.
Most people definitely can't meditate for 30 minutes, so if you can do this, it's very impressive. Regardless, being able to think about poorly-defined problems and build completely new mental models from nothing is genuinely a really hard and uncomfortable task. If you don't use the skill you'll lose it.
Maybe not traditional meditation, but I have no problem taking a 30 minute plus walk with nothing but my thoughts. It’s actually when I do most of my thinking. The other is in the shower/sauna where devices don’t work anyway.
Not saying everyone else is immune, but those a few years older have also had a period without it.
> apparently some of the smartest people in the world have lost the skill?
> But the commenter haven't
> why?
Perhaps because a correlation you assumed was there (more smartness = more ability to sit still alone with one's thoughts), is not actually as strong as you thought? If one does not start with that assumption, there is no inherent conflict in the 3 pieces of evidence you cited.
Or perhaps because you are smarter than you give yourself credit for :)
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 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.)
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
What could go wrong...
It was already discussed on HN.
Could you explain?
From the current article
In addition to overreliance on AI, Garcia also pointed out that many students are underprepared mathematically, a concern echoed by campus associate teaching professor Gireeja Ranade.
From the article discussed the other week:
Over three years — from fall 2021 to fall 2023 — the letter said, at least 20% of Berkeley first-semester calculus students who took a diagnostic exam showed deficits. “Basic mathematical fluency is analogous to literacy; without it, success in university-level STEM becomes structurally unattainable for students,” faculty wrote.
It's been steadily getting worse. The current article only looks at F's which conveniently hides if there has been a slope down. Additionally, kids entering HS in 2021/2022 would just now be hitting college.
I don't believe this is accurate. Failing grades are what the observation entails, and the data clearly depict an abrupt change; not a gradual one.
In the section titled "Failing grades in 3 CS classes skyrocket in spring 2026 ", there's a clear jump in failing grades for all cited courses between 2025 and 2026. Failing grades for every course jump by multiples of the previous year.
The kids who saw the removal of standardized testing 3 years out from going to college never bothered.
Works the other way too - if you introduce something positive in grade 1, you'll only see the results a few years later.
"Failure to complete the qualification" is the prediction.
There are many countries, especially in Europe, where entrace/admission tests are not a thing.
That said, the Sixth Form exams are mostly standardised with only a few different exam boards for the entire country, so the Sixth Form grades end up being something akin to standardised tests anyway.
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.
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.
"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."
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...
Its all Goodhart's law problem, but we are missing the forest for the trees talking about grades and tests when what we want is people to be educated, and critical thinkers and competent in their area and due to a comprehensive way to evaluate that we end up talking about grade inflation or how Yale vs Berkeley gives letters at the end of a semester
>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.
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?
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.
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.
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.
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).
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.
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.
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?
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...
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.)
Did they use AI to detect AI using cheaters?
AI detectors are pretty mid in practice - they tend to have a lot of false positives for "B" students who are okay, but can still be struggled to be more coherent than AIs are. There are some specific triggers that AIs are way more likely to do than students, but a lot of AI detectors will trigger on this "almost there, but you're still struggling" level of essay writing that might get a B, B-.
I could expect the same might be true for CS students even though I haven't seen how AI detectors work for CS/math homework.
It’s not that students didn’t cheat before, LLMs have just lowered the bar so far many can’t complete a live test in a class that requires effort.
It's not AI, its a deterministic program that analyzes compiled code for similarity.
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.
You do need to be good at math to do e.g. physics (or math itself!), nomatter the tools at your disposal.
A bunch of science fiction stories had "first connection to cyberspace" as a coming of age event, maybe those authors were on to something.
Plagiarism isn't new, and those things enabled it too.
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.
Overall it just seems like a huge waste of money to piss away the huge tuition cost your parents probably paid.
The smart ones either use it not at all, or use it to positive effect, like you're saying.
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.
AI apps are very powerful for teaching. You just need to tell them to do that, and not to directly solve your problem.
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
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.
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
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+.
Ironically, the techniques of the latter yield the results of the first, but everybody gets to keep a pure heart.
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.
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.
* Tom Lehrer: New Math (1965) https://www.youtube.com/watch?v=W6OaYPVueW4
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 :(.
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 don't think instruction would've changed drastically in the last year though.
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.
Of course, if a student just breezes through it then I would agree. That would make no sense.
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.
+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.
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.
Artificial Intelligence and Grade Inflation
https://cshe.berkeley.edu/publications/artificial-intelligen...
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).
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...
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...
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.
"Enlightenment is man's emergence from his self-imposed nonage. Nonage is the inability to use one's own understanding without another's guidance."
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.
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.
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