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I think this is very correct for studying, when I was in undergrad, if I saw the answer, I found it was more effective to skip past it and solve it later. Even more important, once I am done, I should not go look at the answer to confirm if I am right or wrong, I should try and validate the answer by looking at my solution and trying to figure out if my solution is correct or not because there are plenty of ways to "disqualify" an answer, once I learned to do this well, my grades really went up.

However, I don't always agree with the fact that we don't have the answer in front of us in many situations. There are a lot of situations beyond grade school and undergrad where you do have the answer in front of you. Sometimes it's in for form of Numpy or Matlab or Simulink. It might be someone's research publication. Replicating these by working both forwards and backwards from their libraries or results can be much more effective.

I think it depends on how the tool is used. If a student is just plugging in the problem and asking for the answer, there is clearly no long term benefit to this. If the student is trying to understand a concept, and uses GPT to bounce ideas around or to ask for alternate ways of thinking about something, it can be very helpful.

How spacing is utilized can be helpful too. Struggle with the problem for 20-30 minutes. Ask for a nudge. Struggle some more. Repeat many times.

Some concepts also just have to be thought about differently to get to the aha moment, especially in math. AI may have an opportunity to present many instances of “think about it this way instead”.

>In life, we rarely have the answer in front of us,

this sounds a lot like the "when you're an adult, you won't always have a calculator in your pocket" line we all heard in elementary school. and of course, we all know now how wrong that was.

in life, chatGPT exists. and tools like it are only going to become more widespread. we are going to have the answers in front of us. knowing the things an LLM knows is not a useful skill anymore.

"It’s this struggling that builds a muscle…"

Very few of us have the luxury or good fortune to achieve anything of substance without effort, as the old saying goes 'no pain, no gain'.

Good teachers help and are essential for encouragement. If or when AI becomes a truly encouraging mentor then it will be useful.

When a kid I recall hearing a recording of Sparky's Magic Piano on the radio which drove home that there's no shortcut and that effort and hard work eventually pay off. For anyone that hasn't heard it there's a copy on the Internet Archive: https://archive.org/details/78_sparkys-magic-piano_henry-bla...

Here's a Wiki synopsis: https://en.m.wikipedia.org/wiki/Sparky%27s_Magic_Piano

> ChatGPT, I suspect, is akin to looking up the answer.

It’s a tool that can be used in a variety of ways. For example, if you have it act as Socrates working through a dialogue where you still have to use your logic to get to the answer, I doubt that is akin to looking up the answer.

In life there usually isn't an answer.

If you know you got it wrong and you don't know why, what's the alternative to working backwards?

An LLM used properly would be like an individualized tutor that knows the subject very well, and learns the student's quirks quickly.

I mean, any pre-prompt telling the LLM to be a good tutor and not just give kids the answer can be trivially bypassed by 8 year olds; but if the session logs were available for (potentially LLM-based) review to check whether they stayed in tutor mode...

Shit, I should wrap a UI around this and sell it.

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Changing things almost always improves results, that is the first rule you need to remember during education testing. Most of the improvements disappear when you make it standard.

This effect likely comes from novelty being more interesting so kids gets more alert, but when they are used to it then it is the same old boring thing and education results go back to normal. Of course things can improve or get worse, but in general it is really hard to say, you need to have a massive advantage over the standard during testing to actually get any real improvements, most of the time you just make things worse.

Reading comprehension is really awful nowadays or people tend to just comprehend what confirms their prior beliefs. The sad part is that none of those people will ever realize the errors in their comprehension of the article. That's exactly one of the mechanisms how people form wrong opinions, it's compounding and almost impossible to change.

The takeaway should be (besides the research still needing reproduction) to encourage the control of the type of AI agent that is given to students, ones that don't just give answers to copy but provide tutoring. OpenAI should be forced to develop such a "student mode" immediately and parents and educators need to be made aware of it to make sure students are using it, otherwise students are going to get much worse in tests, as they just ask it for answers to copy in assignments.

Assuming that the kids with "Human Tutor" were statistically better than control (they were not in the study so we will not know) - this is a very poor showing for ChatGPT.

> frequently feed it my own notes and ask it to correct any misunderstandings, or to expand on things I don’t understand

In what fields? I’ve tried this with some simple finance and aerospace problems; it’s me, sophomore year undergrad, except someone laced everything I drank with LSD.

> LLMs, for me, have been tremendously useful in learning new concepts.

Are you validating this in a rigorous way like this study, or are you just "feeling" like it's useful for your learning?

> Also “access to GPT“ in this case is having GPT open while solving the problems, not studying with GPT and then taking the test

If this is your takeaway you misread the paper. Students have access to GPT (if they have access, the control didn't) while working through practice problems. Not for the exam itself. From the paper in the experimental design section:

> Each session has three parts:

> 1. In the first part, teachers review a topic (e.g., combinatorics) previously covered in the course, and solve one or more examples on the board. This part is identical to a standard high school one-to-many (i.e., teacher-to-students) lecture.

> 2. The second part is an assisted practice period, where students solve a sequence of exercises designed by teachers to reinforce the covered concept. Our randomized intervention (described in more detail below) only affects this second, self-study part.

> 3. The third part is an unassisted evaluation, where students take a closed-book, closed laptop exam. Importantly, each problem in the exam corresponds to a conceptually very similar practice problem from the previous part—this design was chosen to help students practice the key concepts needed to perform well on the exam.

Students with GPT (either form) did better during the practice problem portion and then worse during the actual exam (without GPT access) than students in the control.

They don't do better as far as learning is concerned. Isn't this concerning?

> However, we additionally find that when access is subsequently taken away, students actually perform worse than those who never had access (17% reduction for GPT Base).

The abstract continues:

> That is, access to GPT-4 can harm educational outcomes. These negative learning effects are largely mitigated by the safeguards included in GPT Tutor. Our results suggest that students attempt to use GPT-4 as a "crutch" during practice problem sessions, and when successful, perform worse on their own. Thus, to maintain long-term productivity, we must be cautious when deploying generative AI to ensure humans continue to learn critical skills.

People sophisticated in a field can ask Sonnet or 4o questions that amount to a different way of searching and sometimes even a better one. If you ask a question in a direct, probing, narrow way you can sometimes come out ahead.

Someone educated by the News Feed algorithm (which is what RLHF amounts to: reward for getting human to click) is going to be the worst kind of wrong: /r/ConfidentlyIncorrect.

> Kids who use ChatGPT do actually “significantly” better according to the authors

No, ChatGPT does significantly better than the kids who don't have access to ChatGPT.

Copy-pasting answers from ChatGPT isn't some amazing skill.

You can read the paper itself to get my useful information. The title and article is confusing.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4895486

Glad to hear you were able to find a mechanism that clicked and stuck with it after that! The concept of learning styles for individuals though is a common myth

https://onlineteaching.umich.edu/articles/the-myth-of-learni... https://www.youtube.com/watch?v=rhgwIhB58PA&t=2s

There isn't any evidence that individuals learn best to a single style and generally approaching learning from multiple facets is the best way for everyone to learn!

As far as I understand the current scientific consensus is that learning styles do actually not exist.

How did your friend teach you math visually?

What if the function they were using the tool for is basic reasoning?

I worked at building supplies shop for 5 months when I was a youth, and I still have an uncanny ability to estimate lengths of building materials 20 years later, just from having handled and sorted different sizes of building materials so much.

I remember when I started there the old guys would point at stuff and say the length of it, and I was just amazed that they were correct, even if if it was a long 4.8 meter long plank of wood that they had never seen before. I gained that ability after like 3 months there and I still have a strong ability for it now. Weird random ability.

After skimming through the article - as I understand ChatGPT was used as a tutor.

Many users of ChatGPT clearly know that it doesn't do math.

Now imagine your teacher is wrong at solving 40+% of problems it’s teaching you? Or that your measuring tape is wrong at nearly every second measurement?

Yeah it’s a tool - but you must nail your fundamentals right. Where I grew up - calculators were not allowed in elementary school - so every student must nail down basic arithmetic.

As cool as AI is, the only thing it’s going to do is increase inequality.

I bet all the wealthy and middle class parents with STEM background will get tutoring for their kids in the “old ways” knowing full well that the people who aren’t reliant on AI and can spot mistakes in output will now have a huge advantage in the workforce.

I can report that I have learned more about bash scripting in the past couple of years using ChatGPT to write all manner of scripts, than I did in the previous 20 years of copying and pasting things off StackOverflow.

If your goal is passing a test or rushing through homework then yes. But if you’re actually curious about a topic and understand the limitations of LLMs and how to use prompt language you can actually learn quite a lot from them.

Agreed. I dont want kids to use this until they are much older... it's a crutch. They are not applying critical thinking skills when using a LLM. The value is when children try->fail->try->fail->try->succeed. It builds up problem-solve abilities.

To emphasize, the "GPT Tutor" kids didn't do worse on the exam than the control kids, but they didn't do better either. The effect was slightly negative, statistically insignificant:

> Student performance in the GPT Tutor cohort was statistically indistinguishable from that of the control cohort, and the point estimate was smaller by an order of magnitude (-0.004), suggesting minimal impact to performance in the unassisted exam.

"GPT Base" would provide complete answers, but "GPT Tutor" was prompted to provide only hints. So the result is perhaps that:

1. Given the option to let a machine ("GPT Base") do their homework, many kids will lazily take it. These kids won't learn as much.

2. A machine that refuses to do their homework ("GPT Tutor") doesn't cause that problem. It doesn't seem to help either, though.

I'd guess that laziness would explain most of the harm rather than mistakes made by "GPT Base", though I have no particular evidence for that. Maybe someone will repeat this study in a domain where "GPT Base" makes fewer mistakes, allowing those two effects to be distinguished. (Though would that pass ethics review, now that "GPT Base" is known to impair learning in at least some cases?)

I never was good at hand written notes, but I always outperformed my classmates on tests. Except for the one geometry class where the teacher gave me detention for not taking enough notes. Maybe I'm an exception.

You seemed to leave the part out of your quote where it says those higher percentage of practice problems didn’t translate to test scores.

At best they do the same, at the worst they do worse. There was no scenario where ChatGPT actually helped (unless you count practice problems, which are apparently a meaningless stat, since it doesn’t translate to test scores).

Most kids in the general public would use normal ChatGPT, not one specially optimized to act like a tutor. So in the real world, kids who use it will do worse, and it’s not with seeking out the special one, as there is no advantage.

The title seems like a pretty fair 1 line summary, and pours some much needed cold water on a space that has been full of hype and promises, many of which have fallen flat.

That 127 is when they had access to chatGPT during _practice_.

They fared worse once that access was taken away during the actual exam.

ChatGPT won't be able to help with all situations in the real world though. And there's a chance OpenAI fails and other LLMs become less accessible and more expensive. Future lawsuits and legislation could lead to them becoming cripled. It's a hell of a crutch to teach the next generation to lean on.

I’d argue the opposite, that rarely in the real world is chatGPT easily accessible/usable.

Working with a customer/client? I don’t think you’ll take a minute to say “give me one second let me ask ChatGPT”

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I learned programming before genAI, but with plenty of video tutorials and stackoverflow available.

Someday I noticed that I would automatically hit up the search engine within seconds when I hit a road block. It's kinda the same as with GenAI. You just have to know how to query search engines efficiently. I got stuff done, but I often did not learn much and sometimes didn't even understand what I was doing. So I started to force myself to use data- or reference sheets and try to come up with a solution myself. And if that wasn't good enough or I had real trouble, then I tried using some different resource. And it improved my programming experience tremendously. It's hard, but it's worth it.

> I also find myself copy pasting a question in chat to understand it better.

This is different fromt students relying on ChatGPT to pass tests.

Your use is much more adulty and you're trying to understand before proceeding. You use ChatGPT like a tutor rather than a calculator, which is improving what you know rather than taking from it

Key part of the study is that it gets the logical steps wrong. Worse, it's convincing, so be careful.

LLMs are great for finding key words in a domain you don't know which you can then use to search.

As a mostly self taught programmer the advice I'll give it to read docs and learn to read code. This is when my skills really increased. It's easy to get caught up in trying to just find the answer and doing these things definitely takes longer. But it has a multiplicative effect. You'll not only learn to be able to do it faster but you'll need to do it less often. So never pass up the opportunity to learn. There are times you need to rush but they're far less often than you think.

The other big advice I have is two strategies when coding. First write it quick and fast. Sloppy is okay, you're learning to solve the problem (move fast and break things). Then while you're cleaning up the mess you made, document. While you're doing that it'll stress the lessons into your memory and you'll almost always discovery things you missed. It is literally the rubber ducky method. Plus, you get the benefit of docs (others will read your code. And months or years later you'll come back, asking what idiot wrote this garbage to only find it's you. But also, that's a good sign, because you improved!). The second point is that the best code is flexible code. Remember that you suck at code (the secret is we all do) and you're going to have to come back, edit, and debug. So by writing flexible code you're making life easier for future you. It's really easy to forget these lessons because we think in the moment and our egos don't want us to admit we're bumbling idiots, but like in a video game, when it gets harder it means you're progressing. Don't misinterpret that signal.

The paper at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4895486 says:

> One of the co-authors, Özge Kabakcı, a high school math teacher and former department chair of the math department at our partner Turkish high school, led the development of all session materials.

I guess it would be hard to find a high school with the sample size that you need (thousand) that will agree on collaborating. And in the US every county will have different rules and in terms of math they don't teach it in standard way.

But why Turkish not British or any other place is going to be a question no matter the location. But do you really think the results will be significantly different if it is done lets say on Vietnamese students?

They didn’t have to figure out how to solve the problem. Instead of struggling a bit, which is where the learning happens, they would likely go to ChatGPT for the answer. When the answer bot was taken away, they weren’t prepared to think about how to solve the problem and work it out.

I’ve noticed this even using Copilot in VS Code. I rarely use it, but if I start pulling it out, I notice at the first hint of actually thinking about how to do something, my brain seeks to ask Copilot instead. It’s like there is an off switch that gets flicked when there is an easy button available. If I were to figure it out on my own, I’d know what to do next time I run into a problem like that… if I use Copilot, I’ve learned nothing, other than next time I run into this, use Copilot.

It’s a crutch when it comes to learning.

> they had worked correctly

ChatGPT worked correctly for them but they learned nothing. It should be pretty obvious that you don’t learn by copying answers.

Do kids who have get private tutoring also average worse on tests?

Your characterization of "do their homework" seems wrong as the paper says of the GPT Tutor branch that "is hard for students to use it as a crutch since its prompt asks it to avoid giving them the answer and instead guide them in a step-by-step fashion."

"I blame the parents" s/parents/chatgpt/

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I think they will be the case. When I was a kid my high school math class was very calculator driven. I have a lot to say about that, but won’t bore you with it. I would often see adults pull out a pencil and paper and do a whole bunch of math very quickly, or do a lot of math quickly in their head. I couldn’t do this as well as them, and my math classes weren’t doing anything to move me in that direction. When the calculators were taken away, most of the kids were useless. I saw a guy in National Math Honor Society put 1-1 in his calculator, unironically. This was almost 25 years ago. I assume it’s only gotten worse, and will continue to get worse as more tools that provide answers are given to the students.

I think new tools are great, but it seems to me that the people who can utilize them the best are the ones who learned without them. Those are the people who understand the concepts behind them, so they can ask the right questions, with the right terminology, and they can recognize if an answer makes sense.

When learning is done with these tools, even just calculators, little is being learned. Memorize the button sequence to press leading up to the test. Once the test is over, forget the sequence… and even if the buttons are relented, it won’t matter, because while we all walk around with a calculator in our pockets now, it’s not a TI-83 (or whatever people use now), so without the conceptual knowledge, what good is the button sequence. That doesn’t even prepare a person to leverage ChatGPT well.

Current and previous generations struggle too. Critical thinking and problem solving skills have always been an absolute bear to teach, and few teachers or school systems have the stomach to teach it.

I’m thinking of science education, for example. It’s not just a matter of teaching kids a bunch of disconnected facts about science like “the mitochondria is the powerhouse of the cell” but teaching kids to use the scientific method. To teach the scientific method well, you have to get the kids to design experiments and be comfortable with the prospect of failure. This is, sadly, a very modern approach and requires training the teachers how to do this. Some schools don’t but it’s not something you would have likely gotten much of in the 20th century.

Instead, people are still fighting over dumb shit like which facts are taught in class. “Critical thinking skills” are not on the menu most of the time.

This is my biggest fear of AI technology. That we outsource our reasoning to things that can't.

As a ML researcher myself I'm often baffled with the direction we go with these tools. It feels like we try to use then to do the things humans like and are best at and then have humans do the things ML models are better at and humans hate doing.

The tech is no doubt exciting (it's why I research it!) but that neither justifies unbounded hype nor blind ourselves. But I guess that's the classic engineering problem: it's easy to get lost in the good and exciting parts of what you're building and lose sight of the harm it can do. It's hard to not fall victim to this and I'm sure we're all guilty of it to some extent, I know I am

> It says right there that the students who used chatgpt right, as a tutor, did much better than their peers.

No LLM processing required, just old fashioned critical reading and comprehension (my emphasis added):

>> The students who used it did spectacularly better on the practice problems, solving 127 percent more of them correctly compared with students who did their practice work without any high-tech aids. But on a test afterwards, these AI-tutored students did no better.

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I think the part of overconfidence is actually important here. Looking at the paper Figure 2 shows the number of questions. For all sessions with GPT students averaged 2 messages per problem. Interestingly 20% isn't restating the problem or asking for the answer. Looks like 50-60% of tutor questions are asking for answer or restating problem and they ask more questions.

In my experience, usually where GPT and other LLMs get things wrong is in the steps. I see people are frequently over confident about the results and I've long thought this was a big part of it that the answer may be right but steps to get there are wrong. I see this a lot with river crossing problems. People will often show a prompt that solves it (half the time there's information leakage) and a good portion of those will have the logic errors. But people just look at the answer (btw, this is the reason I claim LLMs don't reason. It's not about getting the answer wrong, it's about the logical steps. That's the real evaluation!)

Personally I think this is good evidence of over fitting (memorization).

I also think this is why you should be careful when using it to code. It's the details that matter a lot. Since LLMs are aggregators I'd like to remind everyone that the average coder and average code is terrible. Hell, even good programmers often suck. Code is hard! Whenever I try using LLMs to code I find that I write lines faster but I end up spending more time debugging and prompt engineering than if I just read the docs and did it myself (this also has the added benefit of the struggle making me remember more). So I'll use it to write things I don't really care about but otherwise I'm not getting how people are finding it so helpful (yes, I read my comment). But I suspect many people are similar to the students in the study.

Well I flagged your comment so right back at you.