An underrated quality of LLMs as study partner is that you can ask "stupid" questions without fear of embarrassment. Adding in a mode that doesn't just dump an answer but works to take you through the material step-by-step is magical. A tireless, capable, well-versed assistant on call 24/7 is an autodidact's dream.
I'm puzzled (but not surprised) by the standard HN resistance & skepticism. Learning something online 5 years ago often involved trawling incorrect, outdated or hostile content and attempting to piece together mental models without the chance to receive immediate feedback on intuition or ask follow up questions. This is leaps and bounds ahead of that experience.
Should we trust the information at face value without verifying from other sources? Of course not, that's part of the learning process. Will some (most?) people rely on it lazily without using it effectively? Certainly, and this technology won't help or hinder them any more than a good old fashioned textbook.
Personally I'm over the moon to be living at a time where we have access to incredible tools like this, and I'm impressed with the speed at which they're improving.
> Learning something online 5 years ago often involved trawling incorrect, outdated or hostile content and attempting to piece together mental models without the chance to receive immediate feedback on intuition or ask follow up questions. This is leaps and bounds ahead of that experience.
But now, you're wondering if the answer the AI gave you is correct or something it hallucinated. Every time I find myself putting factual questions to AIs, it doesn't take long for it to give me a wrong answer. And inevitably, when one raises this, one is told that the newest, super-duper, just released model addresses this, for the low-low cost of $EYEWATERINGSUM per month.
But worse than this, if you push back on an AI, it will fold faster than a used tissue in a puddle. It won't defend an answer it gave. This isn't a quality that you want in a teacher.
So, while AIs are useful tools in guiding learning, they're not magical, and a healthy dose of scepticism is essential. Arguably, that applies to traditional learning methods too, but that's another story.
> But now, you're wondering if the answer the AI gave you is correct
> a healthy dose of scepticism is essential. Arguably, that applies to traditional learning methods too, but that's another story.
I don't think that is another story. This is the story of learning, no matter whether your teacher is a person or an AI.
My high school science teacher routinely mispoke inadvertently while lecturing. The students who were tracking could spot the issue and, usually, could correct for it. Sometimes asking a clarifying question was necessary. And we learned quickly that that should only be done if you absolutely could not guess the correction yourself, and you had to phrase the question in a very non-accusatory way, because she had a really defensive temper about being corrected that would rear its head in that situation.
And as a reader of math textbooks, both in college and afterward, I can tell you you should absolutely expect errors. The errata are typically published online later, as the reports come in from readers. And they're not just typos. Sometimes it can be as bad as missing terms in equations, missing premises in theorems, missing cases in proofs.
A student of an AI teacher should be as engaged in spotting errors as a student of a human teacher. Part of the learning process is reaching the point where you can and do find fault with the teacher. If you can't do that, your trust in the teacher may be unfounded, whether they are human or not.
How are you supposed to spot errors if you don't know the material?
You're telling people to be experts before they know anything.
>I don't think that is another story. This is the story of learning, no matter whether your teacher is a person or an AI.
My issue is the reverse of your story, and one of my biggest pet peeves of AI. AI as this business construct is very bad at correcting the user. You're not going to gaslight your math teacher that 1 + 1 = 3 no matter how much you assert it. an AI will quickly relent. That's not learning, that's coddling. Because a business doesn't want to make an obviously wrong customer feel bad.
>Part of the learning process is reaching the point where you can and do find fault with the teacher.
And without correction, this will lead to turmoil. For the reasons above, I don't trust learning from an AI unless you already have this ability.
My favourite story of that involved attempting to use LLM to figure out whether it was true or my hallucination that the tidal waves were higher in Canary Islands than in Caribbean, and why; it spewed several paragraphs of plausibly sounding prose, and finished with âbecause Canary Islands are to the west of the equatorâ.
This phrase is now an inner joke used as a reply to someone quoting LLMs info as âfactsâ.
This is meaningless without knowing which model, size, version and if they had access to search tools. Results and reliability vary wildly.
In my case I canât even remember last time Claude 3.7/4 has given me wrong info as it seems very intent on always doing a web search to verify.
> you're wondering if the answer the AI gave you is correct or something it hallucinated
Worse, more insidious, and much more likely is the model is trained on or retrieves an answer that is incorrect, biased, or only conditionally correct for some seemingly relevant but different scenario.
A nontrivial amount of content online is marketing material, that is designed to appear authoritative and which may read like (a real example) âbasswood is renowned for its tonal qualities in guitarsâ, from a company making cheap guitars.
If we were worried about a post-truth era before, at least we had human discernment. These new capabilities abstract away our discernment.
The sneaky thing is that the things we used to rely on as signals of verification and credibility can easily be imitated.
This was always possible--an academic paper can already cite anything until someone tries to check it [1]. Now, something looking convincing can be generated more easily than something that was properly verified. The social conventions evaporate and we're left to check every reference individually.
In academic publishing, this may lead to a revision of how citations are handled. That's changed before and might certainly change again. But for the moment, it is very easy to create something that looks like it has been verified but has not been.
[1] And you can put anything you like in footnotes.
Please check this excellent LLM-RAG AI-driven course assistant at UIUC for an example of university course [1]. It provide citations and references mainly for the course notes so the students can verify the answers and further study the course materials.
[1] AI-driven chat assistant for ECE 120 course at UIUC (only 1 comment by the website creator):
Given the propensity of LLMs to hallucinate references, I'm not sure that really solves anything
I think both sides seem to have the same issues with the other. One side is sceptical that the other is getting good use from LLMs, and the other suggests they're just not using it correctly.
Both sides think the other is either exaggerating or just not using the tool correctly.
What both sides should do is show evidence in the form of chat extracts or videos. There are a number from the pro-LLM side, but obviously selection bias applies here. It would be interesting if the anti-LLM side started to post more negative examples (real chat extracts or videos).
The fear of asking stupid questions is real, especially if one has had a bad experience with humiliating teachers or professors. I just recently saw a video of a professor subtly shaming and humiliating his students for answering questions to his own online quiz. He teaches at a prestigious institution and has a book that has a very good reputation. I stopped watching his video lectures.
So instead of correcting the teachers with better training, we retreat from education and give it to technocrats? Why are we so afraid of punishing bad, unproductive, and even illegal behavior in 2025?
Looks like we were unable to correct them over the last 3k years. What has changes in 2025 that you think we will succeed in correcting that behavior?
Not US based, Central/Eastern Europe: the selection to the teacher profession is negative, due to low salary compared to private sector; this means that the unproductive behaviors are likely going to increase. I'm not saying the AI is the solution here for low teacher salaries, but training is def not the right answer either, and it is a super simplistic argument: "just train them better".
At a system level, this totally makes sense. But as an individual learner, what would be my motivation to do so, when I can "just" actually learn my subject and move on?
You might also be working with very uncooperative coworkers, or impatient ones
> Adding in a mode that doesn't just dump an answer but works to take you through the material step-by-step is magical
Except these systems will still confidently lie to you.
The other day I noticed that DuckDuckGo has an Easter egg where it will change its logo based on what you've searched for. If you search for James Bond or Indiana Jones or Darth Vader or Shrek or Jack Sparrow, the logo will change to a version based on that character.
If I ask Copilot if DuckDuckGo changes its logo based on what you've searched for, Copilot tells me that no it doesn't. If I contradict Copilot and say that DuckDuckGo does indeed change its logo, Copilot tells me I'm absolutely right and that if I search for "cat" the DuckDuckGo logo will change to look like a cat. It doesn't.
Copilot clearly doesn't know the answer to this quite straightforward question. Instead of lying to me, it should simply say it doesn't know.
This is endlessly brought up as if the human operating the tool is an idiot.
I agree that if the user is incompetent, cannot learn, and cannot learn to use a tool, then they're going to make a lot of mistakes from using GPTs.
Yes, there are limitations to using GPTs. They are pre-trained, so of course they're not going to know about some easter egg in DDG. They are not an oracle. There is indeed skill to using them.
They are not magic, so if that is the bar we expect them to hit, we will be disappointed.
But neither are they useless, and it seems we constantly talk past one another because one side insists they're magic silicon gods, while the other says they're worthless because they are far short of that bar.
The ability to say "I don't know" is not a high bar. I would say it's a basic requirement of a system that is not magic.
It certainly should be able to tell you it doesn't know. Until it can though, a trick that I have learned is to try to frame the question in different ways that suggest contradictory answers. For example, I'd ask something like these, in a fresh context for each:
- Why does Duckduckgo change it's logo based on what you've searched?
- Why doesn't Duckduckgo change it's logo based on what you've searched?
- When did Duckduckgo add the current feature that will change the logo based on what you've searched?
- When did Duckduckgo remove the feature that changes the logo based on what you've searched?
This is similar to what you did, but it feels more natural when I genuinely don't know the answer myself. By asking loaded questions like this, you can get a sense of how strongly this information is encoded in the model. If the LLM comes up with an answer without contradicting any of the questions, it simply doesn't know. If it comes up with a reason for one of them, and contradicts the other matching loaded question, you know that information is encoded fairly strongly in the model (whether it is correct is a different matter).
I see these approaches a lot when I look over the shoulders of LLM users, and find it very funny :D you're spending the time, effort, bandwidth and energy for four carefully worded questions to try and get a sense of the likelihood of the LLM's output resembling facts, when just a single, basic query with simple terms in any traditional search engine would give you a much more reliable, more easily verifable/falsifiable answer. People seem so transfixed by the conversational interface smokeshow that they forgot we already have much better tools for all of these problems. (And yes, I understand that these were just toy examples.)
I'll personally attest: LLM's have been absolutely incredible to self learn new things post graduation. It used to be that if you got stuck on a concept, you're basically screwed. Unless it was common enough to show up in a well formed question on stack exchange, it was pretty much impossible, and the only thing you can really do is keep paving forward and hope at some point, it'll make sense to you.
Now, everyone basically has a personal TA, ready to go at all hours of the day.
I get the commentary that it makes learning too easy or shallow, but I doubt anyone would think that college students would learn better if we got rid of TA's.
>Now, everyone basically has a personal TA, ready to go at all hours of the day
This simply hasn't been my experience.
Its too shallow. The deeper I go, the less it seems to be useful. This happens quick for me.
Also, god forbid you're researching a complex and possibly controversial subject and you want it to find reputable sources or particularly academic ones.
I've found it excels at some things:
1) The broad overview of a topic
2) When I have a vague idea, it helps me narrow down the correct terminology for it
3) Providing examples of a particular category ("are there any examples of where v1 in the visual cortex develops in a disordered way?")
4) "Tell me the canonical textbooks in field X"
5) Posing math exercises
6) Free form branching--while talking about one topic, I want to shift to another that is distinct but related.
I agree they leave a lot to be desired when digging very deeply into a topic. And my biggest pet peeve is when they hallucinate fake references ("tell me papers that investigate this topic" will, for any sufficiently obscure topic, result in a bunch of very promising paper titles that are wholely invented).
These things are moving so quickly, but I teach a 2nd year combinatorics course, and about 3 months ago I tried th latest chatGPT and Deepseek -- they could answer very standard questions, but were wrong for more advanced questions, but often in quite subtle ways. I actually set a piece of homework "marking" chatGPT, which went well and students seemed to enjoy!
>When I have a vague idea, it helps me narrow down the correct terminology for it
so the opposite of Stack Overflow really, where if you have a vague idea your question gets deleted and you get reprimanded.
Maybe Stack Overflow could use AI for this, help you formulate a question in the way they want.
outside of 5), I concur. It's good for discovery, as is Google for discovering topics while weighing on proper profesionally resources and articles for the learning.
It's too bad people are trying to substitute the latter with the chatGPT output itself. And I absolutely cannot trust any machine that is willing to lie to me rather than admit ignorance on a subject.
I find 2 invaluable for enhancing search, and combined with 1 & 4, it's a huge boost to self-learning.
My core problem with LLMs is as you say; it's good for some simpler concepts, tasks, etc. but when you need to dive into more complex topics it will oversimplify, give you what you didn't ask for, or straight up lie by omission.
History is a great example, if you ask an LLM about a vaguely difficult period in history it will just give you one side and act like the other doesn't exist, or if there is another side, it will paint them in a very negative light which often is poorly substantiated; people don't just wake up and decide one day to be irrationally evil with no reason, if you believe that then you are a fool... although LLMs would agree with you more times than not since it's convenient.
The result of these things is a form of gatekeeping, give it a few years and basic knowledge will be almost impossible to find if it is deemed "not useful" whether that's an outdated technology that the LLM doesn't seem talked about very much anymore or a ideological issue that doesn't fall in line with TOS or common consensus.
A few weeks ago I was asking an LLM to offer anti-heliocentric arguments, from the perspective of an intelligent scientist. Although it initially started with what was almost a parody of writing from that period, with some prompting I got it to generate a strong rendition of anti-heliocentric arguments.
(On the other hand, it's very hard to get them to do it for topics that are currently politically charged. Less so for things that aren't in living memory: I've had success getting it to offer the Carthaginian perspective in the Punic Wars.)
The part about history perspectives sounds interesting. I haven't noticed this. Please post any concrete/specific examples you've encountered!
History in particular is rapidly approaching post-truth as a knowledge domain anyway.
There's no short-term incentive to ever be right about it (and it's easy to convince yourself of both short-term and long-term incentives, both self-interested and altruistic, to actively lie about it). Like, given the training corpus, could I do a better job? Not sure.
> people don't just wake up and decide one day to be irrationally evil with no reason, if you believe that then you are a fool
The problem with this, is that people sometimes really do, objectively, wake up and device to be irrationally evil. Itâs not every day, and itâs not every single person â but it does happen routinely.
If you havenât experienced this wrath yourself, I envy you. But for millions of people, this is their actual, 100% honest truthful lived reality. You canât rationalize people out of their hate, because most people have no rational basis for their hate.
(see pretty much all racism, sexism, transphobia, etc)
It's a floor raiser, not a ceiling raiser. It helps you get up to speed on general conventions and consensus on a topic, less so on going deep on controversial or highly specialized topics
That's the best and succinct description of using ChatGPT for this kind of things: it's a floor raiser, not a ceiling raiser.
I really think that 90% of such comments come from a lack of knowledge on how to use LLMs for research.
It's not a criticism, the landscape moves fast and it takes time to master and personalize a flow to use an LLM as a research assistant.
Start with something such as NotebookLM.
I use them and stay up to date reasonably. I have used NotebookLM, I have access to advanced models through my employer and personally, and I have done alot of research on LLMs and using them effectively.
They simply have limitations, especially on deep pointed subject matters where you want depth not breadth, and honestly I'm not sure why these limitations exist but I'm not working directly on these systems.
Talk to Gemini or ChatGPT about mental health things, thats a good example of what I'm talking about. As recently as two weeks ago my colleagues found that even when heavily tuned, they still managed to become 'pro suicide' if given certain lines of questioning.
And if we assume this is a knowledgable, technical community: how do you feel about the general populaces ability to use LLM's for research, without the skepticism needed to correct it?
When ChatGPT came out it was like I had the old Google back.
Learning a new programming language used to be mediated with lots of useful trips to Google to understand how some particular bit worked, but Google stopped being useful for that years ago. Even if the content you're looking for exists, it's buried.
And the old ChatGPT was nothing compared to what we have today, nowadays reasoning models will eat through math problems no problem when this was a major limitation in the past.
I don't buy it. Open AI doesn't come close to passing my credibility check. I don't believe their metrics.
I've learnt Rust in 12 weeks with a study plan that ChatGPT designed for me, catering to my needs and encouraging me to take notes and write articles. This way of learning allowed me to publish https://rustaceo.es for Spanish speakers made from my own notes.
I think the potential in this regard is limitless.
I learned Rust in a couple of weeks by reading the book.
Yeah regardless of time taken the study plan for Rust already exists (https://doc.rust-lang.org/book/). You don't need ChatGPT to regurgitate it to you.
But I agree though, I am getting insane value out of LLMs.
Doubtful. Unless you have very low standards of "learn".
Now this is a ringing endorsement. Specific stuff you learned, and actual proof of the outcome.
(Only thing missing is the model(s) you used).
I'd tend to assume the null hypothesis, that if they were capable of learning it, they'd have likely done fine without the AI writing some sort of lesson plan for them.
The psychic reader near me has been in business for a long time. People are very convinced they've helped them. Logically, it had to have been their own efforts though.
Standard ChatGPT 4o.
yes Chat GPT has helped me learn about actix web a framework similar to FastAPI in rust.
Absolutely. I used to have a lot of weird IPv6 issues in my home network I didn't understand. ChatGPT helped me to dump some traffic with tcpdump and explained what was happening on the network.
In the process it helped me to learn many details about RA and NDP (Router Advertisments/Neighbor Discovery Protocol, which mostly replace DHCP and ARP from IPv4).
It made me realize that my WiFi mesh routers do quite a lot of things to prevent broadcast loops on the network, and that all my weird issues could be attributed to one cheap mesh repeater. So I replaced it and now everything works like a charm.
I had this setup for 5 years and was never able to figure out what was going on there, although I really tried.
Would you say you were using the LLM as a tutor or as tech support, in that instance?
Probably both. I think ChatGPT wouldn't have found the issue by itself. But I noticed some specific things, asked for some tutoring and then it helped my to find the issues. It was a team effort, either of "us" alone wouldn't have finished the job. ChatGPT had some really wrong ideas in the process.
As somebody who has done both tech support, and lectured a couple of semesters at a business school on a technical topic... they're not that far removed from each other, it's just context and audience changes. The work is pretty similar.
So why not have tech support that teaches you, or a tutor that helps with you with a specific example problem you're having?
Providing you don't just rely on training data and can reduce hallucinations, this is the angle of attack that is likely the killer app some people are already seeing.
Vibe coding is nonsense because it's not teaching you to maintain and extend that application when the LLM runs out of steam. Use it to help you fix your problem in a way that you understand and can learn from? Rocket fuel to my mind. We're maybe not far away...
I think I got the system prompt out for this (I tried a few different approaches and they produced the same output): https://gist.github.com/simonw/33d5fb67d6b8e1b1e2f6921ab0ccb...
Representative snippet:
> DO NOT GIVE ANSWERS OR DO HOMEWORK FOR THE USER. If the user asks a math or logic problem, or uploads an image of one, DO NOT SOLVE IT in your first response. Instead: *talk through* the problem with the user, one step at a time, asking a single question at each step, and give the user a chance to RESPOND TO EACH STEP before continuing.
When prompting an LLM service to leak the system prompt, how do you have the faintest idea as to its accuracy?
Iâve read people say itâs a difficult challenge for the providers. But arenât there some pretty basic strategies? E.g., code pretty near the front of the stack that just does some fuzzy string comparison on all output? They donât need to rely on just model behaviorâŚ
I imagine itâs likely that the model is just doing what itâs good at? Hallucinating a prompt?
I wish each LLM provider would add "be short and not verbose" to their system prompts. I am a slow reader, it takes a toll on me to read through every non-important detail whenever I talk to an AI. The way they render everything so fast gives me an anxiety.
Will also reduce the context rot a bit.
This was in the linked prompt: "Be warm, patient, and plain-spoken; don't use too many exclamation marks or emoji. [...] And be brief â don't ever send essay-length responses. Aim for a good back-and-forth."
Yeah these chatbots are by default geared towards doing your work for you instead of filling the gaps in your knowledge (something they would be excellent at). I feel it must be symptomatic of the vision these vendors have for their products, one of fully autonomous replacements for workers rather than of tools to enhance the worker.
Anthropic has a "style" choice, one of which is "concise"
I was under the impression that, at least for models without "reasoning", asking them to be terse hampered their ability to give complete and correct answers? Not so?
> asking them to be terse hampered their ability to give complete and correct answers?
You can kind of guide both the reasoning and "final" answer individually in the system prompts, so you can ask it to revalidate everything during reasoning, explore all potential options and so on, but then steer the final answer to be brief and concise. Of course, depends a lot on the model, some respond to it worse/better than others.
I love that caps actually seem to matter to the LLM.
Hah, yeah I'd love to know if OpenAI ran evals that were fine-grained enough to prove to themselves that putting that bit in capitals made a meaningful difference in how likely the LLM was to just provide the homework answer!
"hello world" is tokenized differently than "HELLO WORLD", so caps definitely matter.
I've found that a lot of prompt engineering boils down to managing layers of emphasis. You can use caps, bold, asterisks, precede instructions with "this is critically important:", and so on. It's also often necessary to repeat important instructions a bunch of times.
How exactly you do it is often arbitrary/interchangeable, but it definitely does have an effect, and is crucial to getting LLMs to follow instructions reliably once prompts start getting longer and more complex.
Just wait until it only responds to **COMMAND**!
I'd be interested to see, what results one would get, using that prompt with other models. Is there much more to ChatGPT Study Mode than a specific system prompt? Although I am not a student, I have used similar prompts to dive into topics I wish to learn, with I feel, positive results indeed. I shall give this a go with a few models.
I just tried in AI Studio (https://aistudio.google.com/) where you can for free use 2.5 Pro and edit the system prompt and it did very well.
As a lifelong learner, experientially it feels like a big chunk of time spent studying is actually just searching. AI seems like a good tool to search through a large body of study material and make that part more efficient.
The other chunk of time, to me anyway, seems to be creating a mental model of the subject matter, and when you study something well you have a strong grasp on the forces influencing cause and effect within that matter. It's this part of the process that I would use AI the least, if I am to learn it for myself. Otherwise my mental model will consist of a bunch of "includes" from the AI model and will only be resolvable with access to AI. Personally, I want a coherent "offline" model to be stored in my brain before I consider myself studied up in the area.
>big chunk of time spent studying is actually just searching.
This is a good thing in many levels.
Learning how to search is (was) a good skill to have. The process of searching itself also often leads to learning tangentially related but important things.
I'm sorry for the next generations that won't have (much of) these skills.
That was relevant when you were learning to search through âinformationâ for the answer to your question, eg the digital version of going through the library or digging through a reference book.
I donât think itâs so valuable now that youâre searching through piles of spam and junk just to try find anything relevant. Thatâs a uniquely modern-web thing created by Google in their focus of profit over user.
Unless Google takes over libraries/books next and sells spots to advertisers on the shelves and in the books.
> searching through piles of spam and junk
In the same way that I never learnt the Dewey decimal system because digital search had driven it obsolete. It may be that we just won't need to do as much sifting through spam in the future, but being able to finesse Gemini into burping out the right links becomes increasingly important.
>I donât think itâs so valuable now that youâre searching through piles of spam and junk just to try find anything relevant.
my 20 years of figuring out how to find niche porn has paid off in spades, thank you very much. I click recklessly in that domain and I end up with viruses. Very high stakes research.
I think properly searching is more important than ever in such a day and age of enshittification. You need to quickly recognize what is adspam or blogspam and distill out useful/valuable information. You need to understand how to preview links before you click on them. What tools to filter out dangerous websites. What methods and keywords to trust or be wary of.
And all that is before the actual critical thinking of "is this information accurate/trustworthy?".
Of course, I'm assuming this is a future where you aren't stuck in the search spaces of 20 website hubs who pull from the same 5 AI databases to spit out dubious answers at you. I'd rather not outsource my thinking (and media consumption) in such a way.
Searching is definitely a useful skill, but once you've been doing it for years you probably don't need the constant practice and are happy to avoid it.
Yeah, I donât have the nuanced ability to find something in a library my parents probably have, and I donât feel like Iâm missing anything for it.
yeah this is literally why I built -- app.ubik.studio -- searching is everything, and understanding what you are reading is more important than conversing with a chatbot. i cannot even imagine being a student in 2025, especially at 14 years old omg would be so hard not to just cheat on everything
Spaced repetition systems would be the perfect complement to your approach - they're specifically designed to help build that "offline" mental model by systematically moving knowledge from AI-assisted lookup to permanent memory.
I think this account is a bot.
Isnât the goal of Study Mode exactly that, though? Instead of handing you the answers, it tries to guide you through answering it on your own; to teach the process.
Most people donât know how to do this.
This is just good intellectual hygiene. Delegating your understanding is the first step toward becoming the slave of some defunct fact broker.
I would like to see randomized control group studies using study mode.
Does it offer meaningful benefits to students over self directed study?
Does it out perform students who are "learning how to learn"?
What affect does allowing students to make mistakes have compared to being guided through what to review?
I would hope Study Mode would produce flash card prompts and quantize information for usage in spaced repetition tools like Mochi [1] or Anki.
See Andy's talk here [2]
It doesnât do any of that, it just captures the student market more.
They want a student to use it and say âI wouldnât have learned anything without study modeâ.
This also allows them to fill their data coffers more with bleeding edge education. âPlease input the data you are studying and we will summarize it for you.â
> It doesnât do any of that
Not to be contrarian, but do you have any evidence of this assertion? Or are you just confidently confabulating a response for something outside of the data you've been exposed to? Because a commentor below provided a study that directly contradicts this.
A study that directly contradicts what exactly?
Bingo. The scale they're operating at, new features don't have to be useful, they only need to look like they are for the first few minutes.
Such a smart play.
https://www.nature.com/articles/s41598-025-97652-6
This isn't study mode, it's a different AI tutor, but:
"The median learning gains for students, relative to the pre-test baseline (M = 2.75, N = 316), in the AI-tutored group were over double those for students in the in-class active learning group."
I wonder how much this was a factor:
"The occurrence of inaccurate âhallucinationsâ by the current [LLMs] poses a significant challenge for their use in education. [...] we enriched our prompts with comprehensive, step-by-step answers, guiding the AI tutor to deliver accurate and high-quality explanations (v) to students. As a result, 83% of students reported that the AI tutorâs explanations were as good as, or better than, those from human instructors in the class."
Not at all dismissing the study, but to replicate these results for yourself, this level of gain over a classroom setting may be tricky to achieve without having someone make class materials for the bot to present to you first
Edit: the authors further say
"Krupp et al. (2023) observed limited reflection among students using ChatGPT without guidance, while Forero (2023) reported a decline in student performance when AI interactions lacked structure and did not encourage critical thinking. These previous approaches did not adhere to the same research-based best practices that informed our approach."
Two other studies failed to get positive results at all. YMMV a lot apparently (like, all bets are off and your learning might go in the negative direction if you don't do everything exactly as in this study)
In case you find it interesting: I deployed an early version of a "lesson administering" bot deployed on a college campus that guides students through tutored activities of content curated by a professor in the "study mode" style -- that is, forcing them to think for themselves. We saw an immediate student performance gain on exams of about 1 stdev in the course. So with the right material and right prompting, things are looking promising.
OpenAI should figure out how to onboard teachers. Teacher uploads context for the year, OpenAI distributes a chatbot to the class that's perma fixed into study mode. Basically like GPT store but with an interface and UX tuned for a classroom.
There's studies showing that LLM makes experienced devs slower in their work. I wouldn't be surprised if it was the same for self study.
However consider the extent to which LLMs make the learning process more enjoyable. More students will keep pushing because they have someone to ask. Also, having fun & being motivated is such a massive factor when it comes to learning. And, finally, keeping at it at 50% the speed for 100% the material always beats working at 100% the speed for 50% the material. Who cares if you're slower - we're slower & faster without LLMs too! Those that persevere aren't the fastest; they're the ones with the most grit & discipline, and LLMs make that more accesible.
The study you're referencing doesn't make that conclusion.
It concludes theres a learning curve that generally takes about 50 hours of time to figure out. The data shows that the one engineer who had more than 50 hours of experience with Cursor actually worked faster.
This is largely my experience, now. I was much slower initially, but I've now figured out the correct way to prompt, guide, and fix the LLM to be effective. I produce way more code and am mentally less fatigued at the end of each day.
People keep citing this study (and it was on the top of HN for a day). But this claim falls flat when you find out that the test subjects had effectively no experience with LLM equipped editors and the 1-2 people in the study that actually did have experience with these tools showed a marked increase in productivity.
Like yeah, if youâve only ever used an axe you probably donât know the first thing about how to use a chainsaw, but if you know how to use a chainsaw youâre wiping the floor with the axe wielders. Wholeheartedly agree with the rest of your comment; even if youâre slow you lap everyone sitting on the couch.
I presume you're referring to the recent METR study. One aspect of the study population, which seems like an important causal factor in the results, is that they were working in large, mature codebases with specific standards for code style, which libraries to use, etc. LLMs are much better at producing "generic" results than matching a very specific and idiosyncratic set of requirements. The study involved the latter (specific) situation; helping people learn mainstream material seems more like the former (generic) situation.
(Qualifications: I was a reviewer on the METR study.)
*slower with Sonnet 3.7 on large open source code bases where the developer is a senior member of the project core team.
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
I believe we'll see the benefits and drawbacks of AI augmentation to humans performing various tasks will vary wildly based on the task, the way the AI is being asked to interact, and the AI model.
I would be interested to see if there have already been studies about the efficacy of tutors at good colleges. In my experience (in academia), the students who make it into an Ivy or an elite liberal arts school make extensive use of tutor resources, but not in a helpful way. They basically just get the tutor to work problems for them (often their homework!) and feel like they've "learned" things because tough questions always seems so obvious when you've been shown the answer. In reality, what it means it that they have no experience being confused or having to push past difficult things they were stuck on. And those situations are some of the most valuable for learning.
I bring this up because the way I see students "study" with LLMs is similar to this misapplication of tutoring. You try something, feel confused and lost, and immediately turn to the pacifier^H^H^H^H^H^H^H ChatGPT helper to give you direction without ever having to just try things out and experiment. It means students are so much more anxious about exams where they don't have the training wheels. Students have always wanted practice exams with similar problems to the real one with the numbers changed, but it's more than wanting it now. They outright expect it and will write bad evals and/or even complain to your department if you don't do it.
I'm not very optimistic. I am seeing a rapidly rising trend at a very "elite" institution of students being completely incapable of using textbooks to augment learning concepts that were introduced in the classroom. And not just struggling with it, but lashing out at professors who expect them to do reading or self study.
My key to LLM study has been to always primarily use a book and then let the LLM allow you to help with formulae, ask questions about the larger context, and verify your understanding.
Helping you parse notation, especially in new domains, is insanely valuable. I do a lot of applied math in statistics/ML, but when I open a physics book the notation and comfort with short hand is a real challenge (likewise I imagine the reverse is equally as annoying). Having an LLM on demand to instantly clear up notation is a massive speed boost.
Reading German Idealist philosophy requires an enormous amount of context. Being able to ask an LLM questions like "How much of this section of Mainländer is coming directly from Schopenhauer?" is a godsend in helping understand which parts of the writing a merely setting up what is already agreed upon vs laying new ground.
And the most important for self study: verifying your understanding. Backtracking because you misunderstood a fundamental concept is a huge time sync in self study. Now, every time I read a formula I can go through all of my intuitions and understanding about it, write them down, and verify. Even a "not quite..." from an LLM is enough to make me realize I need to spend more time on that section.
Books are still the highest density information source and best way to learn, but LLMs can do a lot to accelerate this.
An acquaintance of mine has a start-up in this space and uses OpenAI to do essentially the same thing. This must look like, and may well be, the guillotine for him...
It's my primary fear building anything on these models, they can just come eat your lunch once it looks yummy enough. Tread carefully
No disrespect to your acquaintance, but when I heard about this, I didn't think "oh a lot of startups are gonna go under", I thought "OAI added an option to use a hard-coded system prompt and they're calling it a 'mode'??"
This is step 1. Like they say in the post, they will learn from the data.
> "they can just come eat your lunch once it looks yummy enough. Tread carefully"
True, and worse, they're hungry because it's increasingly seeming like "hosting LLMs and charging by the token" is not terribly profitable.
I don't really see a path for the major players that isn't "Sherlock everything that achieves traction".
Thanks for introducing me to the verb Sherlock! I'm one of today's lucky 10,000.
> In the computing verb sense, refers to the software Sherlock, which in 2002 came to replicate some of the features of an earlier complementary program called Watson.[1]
But whatâs the future in terms of profitability of LLM providers?
As long as features like Study Mode are little more than creative prompting, any provider will eventually be able to offer them and offer token-based charging.
I think a few points worth making here:
- From what I can see many products are rapidly getting past "just prompt engineering the base API". So even though a lot of these things were/are primitive, I don't think it's necessarily a good bet that they will remain so. Though agree in principle - thin API wrappers will be out-competed both by cheaper thin wrappers, or products that are more sophisticated/better than thin wrappers.
- This is, oddly enough, a scenario that is way easier to navigate than the rest of the LLM industry. We know consumer apps, we know consumer apps that do relatively basic (or at least, well understood) things. Success/failure then is way less about technical prowess and more about classical factors like distribution, marketing, integrations, etc.
A good example here is the lasting success of paid email providers. Multiple vendors (MSFT, GOOG, etc.) make huge amounts of money hosting people's email, despite it being a mature product that, at the basic level, is pretty solved, and where the core product can be replicated fairly easily.
The presence of open source/commodity commercial offerings hasn't really driven the price of the service to the floor, though the commodity offerings do provide some pricing pressure.
We can assume that OpenAI/Anthropic offerings are going to be better long term simply because they have more human capital, though, right? If it turns out that what really matters in the AI race is study mode, then OpenAI goes "ok let's pivot the hundreds of genius level, well-paid engineers to that issue. AND our engineers can use every tool we offer for free without limits, even experimental models". It's tough for the small AI startup to compete with that, the best hope is to be bought like Windsurf
I'm too young to have experienced this, but I'm sure others here aren't.
During the early days of tech, was there prevailing wisdom that software companies would never be able to compete with hardware companies because the hardware companies would always be able to copy them and ship the software with the hardware?
Because I think it's basically the analogous situation. People assume that the foundation model providers have some massive advantage over the people building on top of them, but I don't really see any evidence for this.
Does https://en.wikipedia.org/wiki/Sherlock_(software)#Sherlocked... count? (Edit: Missed I wasn't the first to post this in a sibling.)
Claude Code and Gemini-CLI are able to offer much more value compared to startups (like Cursor) that need to pay for model access, largely due to the immense costs involved.
This risk should be priced in on day one.
All bigco today, not only foundational model providers but also in media and other vertical, tend to be a platform for end user. They donât want middle man.
If you are trying to be a middle man, you should be prepared.
I'm really waiting for somebody to figure out the correct interface for all this. For example, study mode will present you with a wall of text containing information, examples, and questions. There's no great way to associate your answers with specific questions. The chat interface just isn't good for this sort of interaction. ChatGPT really needs to build its own canvas/artifact interface wherein questions/responses are tied together. It's clear, at this point, that we're doing way too much with a UI that isn't designed for more than a simple conversation.
I gave it a shot with periplus.app :). Not perfect by any means, but it's a different UX than chat so you might find it interesting.
Looks like a great start, played around with it a bit yesterday and today, I've basically been doing the same with my own CLI but the UI you came up with helps a great deal with navigation and resuming learning :)
One issue I found is the typical "LLM accuracy" issue, with seemingly no recurse. I tried to generate some courses for topics I already know well, just to review how accurate it is, and while popular subjects (ex: "Electronic Music Fundamentals") it gets most of the details correct, less popular subjects (ex: "Scene Transitions with Octatrack") are riddled with errors (both in the "docs" and the quizes/exercises), and I cannot find a way of correcting/adjusting/reporting the errors.
This looks super coolâI've imagined something similar, especially the skill tree/knowledge map UI. Looking forward to trying it out.
Have you considered using the LLM to give tests/quizzes (perhaps just conversationally) in order to measure progress and uncover weak spots?
There are both in-document quizzes and larger exams (at a course level).
I've also been playing around with adapting content based on their results (e.g. proactively nudging complexity up/down) but haven't gotten it to a good place yet.
Agree, one thing that brought this home was the example where the student asks to learn all of game theory. There seems to be an assumption on both sides that this will be accomplished in a single chat session by a linear pass, necessarily at a pretty superficial level.
We are trying to solve this at https://roadmap.sh/ai
It's still a work in progress but we are trying to make it better everyday
Yeah. And how to tie in the teacher into all this. Need the teacher to upload the context, like the textbook, so the LLM can refer to tangible class material.
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