A Student's Guide to Writing with ChatGPT

I was talking to a teacher today that works with me at length about the impact of AI LLM models are having now when considering student's attitude towards learning.

When I was young, I refused to learn geography because we had map applications. I could just look it up. I did the same for anything I could, offload the cognitive overhead to something better -- I think this is something we all do consciously or not.

That attitude seems to be the case for students now, "Why do I need to do this when an LLM can just do it better?"

This led us to the conclusion:

1. How do you construct challenges that AI can't solve? 2. What skills will humans need next?

We talked about "critical thinking", "creative problem solving", and "comprehension of complex systems" as the next step, but even when discussing this, how long will it be until more models or workflows catch up?

I think this should lead to a fundamental shift in how we work WITH AI in every facet of education. How can a human be a facilitator and shepherd of the workflows in such a way that can complement the model and grow the human?

I also think there should be more education around basic models and how they work as an introductory course to students of all ages, specifically around the trustworthiness of output from these models.

We'll need to rethink education and what we really desire from humans to figure out how this makes sense in the face of traditional rituals of education.

> more education around basic models and how they work

yes, I think this is critical. There's a slate star codex article "Janus Simulators" that explains this very well, that I rewrote to make more accessible to people like my mom. It's not hard to explain this to people, you just need to let them interact with a base model, and explore its quirks. It's a game, people are good at learning systems that they can get immediate feedback from.

> We talked about "critical thinking", "creative problem solving", and "comprehension of complex systems" as the next step, but even when discussing this, how long will it be until more models or workflows catch up?

Either these things are important to learn for their own sake or they aren’t. If the former, then nothing about these objectives needs changing, and if the latter then education itself will be a waste of time.

> I refused to learn geography because we had map applications

Which is ironic, because geography isn’t about memorizing maps

There's so much dystopian science fiction about people being completely helpless because only machines know how to do everything. Then the machines break down.

Some parts of it were though.

> geography

In Germany the subject is called "Erdkunde" which would translate to geology. And this term is, I assume, more appropriate as it isn't just about what is where but also about geological history and science and how volcanoes work and how to read maps and such.

Almost all parts require it, but none are about it. That's how background knowledge works. If you can't get over the drudgery of learning scales and chords, you'll never learn music. The fact that many learners never understand this end goal is sad but doesn't invalidate the methodology needed to achieve the progression.

Pump Six (by Paolo Bacigalupi) comes into my mind.

“The Feeling of Power” is excellent and should be mandatory reading in English classes from here on out.

The correct answer, and you'd see it if folks paid attention to the constant linkedin "AI researcher/ML Engineer job postings are up 10% week over week" banners, is to aggressively reorient education in society to education about how to use AI systems.

This rustles a TON of feathers to even broach as a topic, but it's the only correct one. The AI engineer will eat everything, including your educational system, in 5-10 years. You can either swim against the current and be ate by the sharks or swim with it and survive longer. I'll make sure my kids are learning about AI related concepts from the very beginning.

This was also the correct way to handle it circa the calculator era. We should have made most people get very good at using calculators, and doing "computational math" since that's the vast majority of real world math that most people have to do. Imagine a world where Statistics was primarily taught with Excel/R instead of with paper. It'd be better, I promise you!

But instead, we have to live in a world of luddites and authoritarians, who invent wonderful miracle tools and then tell you not to use them because you must struggle. The tyrant in their mind must be inflicted upon those under them!

It is far better to spend one class period, teaching the rote long multiplication technique, and then focus on word problems and applications of using it (via calculator), than to literally steal the time of children and make them hate math by forcing them to do times tables, again and again. Luddites are time thieves.

Times tables aren’t the problem. Memorizing is actually fun and empowering if done right.

But I agree that the “learning is pain” is just not my experience.

I think at a certain point, you either value having your own skills and knowledge, or you don't. You may as well ask why anyone bothers learning to throw a baseball when they could just offload to a pitching machine.

And I get it. Pitchers who go pro get paid a lot and aren't allowed to use machines, so that's a hell of an incentive, but the vast majority of kids who ever pick up a baseball are never going to go pro, are never even going to try to go pro, and just enjoy playing the game.

It's fair to say many, if not most, students don't enjoy writing the way kids enjoy playing games, but at the same time, the point was mostly never mastering the five paragraph thesis format anyway. The point was learning to learn, about arbitrary topics, well enough to the point that you could write a reasonably well-argued paper about it. Even if a machine can do the writing for you, it can't do the learning for you. There's either value in having knowledge in your own brain or there isn't. If there isn't, then there never was, and AI didn't change that. You always could have paid or bullied the smarter kids into doing the work for you.

I think there is a bit of a 3rd category as well:

1. What can tools do better now that no human could hope to compete with?

2. Which other tasks are likely to remain human-led in the near term?

3. For the areas where tools excel, what is the optimum amount of background understanding to have?

E.g. you mention memorizing maps. Memorizing all of the countries and their main cities is probably not very optimal for 99.999%+ of people vs referencing a map app. At the same time needing to pull up a map for any mention of a location outside of "home" is not necessarily optimal just because the map will have it. And of course the other things about maps in general (types, features, limitations, ways to use them, ways they change) outside of a particular app implementation that would go along with general geography.

I'm not sure I understand the geography point - maps and indexes have been around for hundreds of years - what did the app add to make it not worthwhile learning geography?

How did the people who wrote the LLM and associated software do it when they had no such thing to "just look it up"?

The Machine Stops by E. M. Forster is another very good one:

https://www.cs.ucdavis.edu/~koehl/Teaching/ECS188/PDF_files/...

And re-skimming it just now I noticed the following eerie line:

> There was the button that produced literature.

Wild that this was written in 1903.

> We should have made most people get very good at using calculators, and doing "computational math" since that's the vast majority of real world math that most people have to do.

I strongly disagree. I've seen the impact of students who used calculators to the point they limited their ability to do math. When presented with math in other fields, ones where there isn't a simple equation to plug into a calculator, they fail to process the math because they don't have the number sense. Things like looking over a few experiments in chemistry and looking for patterns become a struggle because noticing the implication that 2L of hydrogen and 1L of oxygen create 2L of water vapor being the same as 2 parts hydrogen plus 1 part oxygen creates 2 part water, which then means that 2 molecules of hydrogen plush 1 molecule of oxygen create 2 molecules of water, all of this implying that 1 molecule of oxygen has to be made of some even number of oxygen atoms so that it can be split in half to make up the 2 water molecules which must have the same amount of oxygen atoms in both. (This is part of a larger series of problems relating to how chemist work out the empirical formula in the past, eventually leading to the molecular formula, and then leading to discovering molecular weight and a whole host of other properties we now know about atoms.)

Without these skills, they are able to build the techniques needed to solve newer harder problems, much less do independent work in the related fields after college.

>Imagine a world where Statistics was primarily taught with Excel/R instead of with paper. It'd be better, I promise you!

I had to take two very different stats classes back in college. One was the raw math, the other was how to plug things into a tool and get an answer. The one involving the tool was far less useful. People learned how to use the tool for simple test cases, but there was no foundation for the larger problems or critiquing certain statistical methodologies. Things like the underlying assumptions of the model weren't touched, meaning students would have had a much harder time when dealing with a population who greatly differed from the assumption.

Rote repetition may not be the most efficient way to learn something, but that doesn't mean avoiding learning it and letting a machine do it for you is better.

> When I was young, I refused to learn geography because we had map applications. I could just look it up. I did the same for anything I could, offload the cognitive overhead to something better -- I think this is something we all do consciously or not.

This is certainly useful to a point, and I don't recommend memorizing a lot of trivia, but it's easy to go too far with it. Having a basic mental model about many aspects of the world is extremely important to thinking deeply about complex topics. Many subjects worth thinking about involve interactions between multiple domains and being able to quickly work though various ideas in your head without having to stop umpteen times can make a world of difference.

To stick with the maps example, if you're reading an article about conflict in the Middle East it's helpful to know off the top of your head whether or not Iran borders Canada. There are plenty of jobs in software or finance that don't require one to be good at mental math, but you're going to run into trouble if you don't at least grok the concept of exponential growth or have a sense for orders of magnitude.

I remember seeing a paper (https://pmc.ncbi.nlm.nih.gov/articles/PMC4274624/) that talked about how physical writing helps kids learn to read later. Typing on a keyboard did not have the same effect.

I expect the same will happen with math and numbers. To be fair you said primarily so you did not imply to do completely away with the paper. I am not certain though that we can do completely away with at least some pain. All the skills I acquired usually came with both frustration and joy.

I am all for trying new methods to see if we can do something better. I have no proof either way though that going 90% excel would help more people learn math. People will run both experiments and we will see how it turns out in 20 years.

Stackoverflow/stack exchange was a proto-LLM. Basically the same thing but 1-2 day latency for replies.

In 20 years we'll be able to tell this in a stereotypically old geezer way: "You kids have it easy, back in my day we had to wait for an actual human to reply to our daft questions.. and sometimes nobody would bother at all!"

Helpful in terms of what? Understanding some forced meme? "Force this meme so you can understand this other forced meme." is not education it's indoctrination. And even if you wanted to, for some unknown reason, understand the article you can look at a (changing and disputed) map as the parent said.

This is the opposite of deep knowledge, this is API knowledge at best.

geolocation, search, path/route finding.

I don't really care to memorize (which was most of the coursework) things which I can just easily look up. Maybe geography in the south was different than how it was taught elsewhere though.

yeah search in general, bulletin boards, shared knowledge bases, etc.

So maybe if there isn't a perceived value in the way we learn, then how learning is taught should maybe change to keep itself relevant as it's not about what we learn, but how we learn to learn.

Are you referring to: > if you're reading an article about conflict in the Middle East it's helpful to know off the top of your head whether or not Iran borders Canada ?

Perhaps, but in the case that you are I think it's a stretch to say that the only utility of this is 'indoctrination' or 'understanding this. other forced meme'. The point is that lookups (even to an AI) cost time, and if you have to do one for every other line in a document, you will either end up spending a ton of time reading, or (more likely) do an insufficient number of lookups and come away with a distorted view of the situation. This 'baseline' level of knowledge IMO is a reasonable thing to expect for any field, not 'indoctrination' in anything other than the most diluted sense of the term.

I think that the classic of the genre is "The feeling of power" (https://en.wikipedia.org/wiki/The_Feeling_of_Power).

> The correct answer, and you'd see it if folks paid attention to the constant linkedin "AI researcher/ML Engineer job postings are up 10% week over week" banners

This does not really lend great credence to the rest of your argument. Yes, Linkedin is hyping the latest job trend. But study after study shows that the bulk of engineers are not doing ML/AI work, even after a year of Linkedin putting up those banners -- and if there were even 2 ML/AI jobs at the start of such a period, then 10% week-over-week growth would imply that the entire population of the earth was in the field.

Clearly that is not the case. So either those banners are total lies, or your interpretation of exponential growth (if something grows exponentially for a bit, it must keep growing exponentially forever) is practically disjointed from reality. And at that point, it's worth asking: what other assumptions about exponential growth might be wrong in this world-view?

Perhaps by "AI engineer" you (like many publications nowadays) just mean to indicate "someone who works with computers"? In that case I could understand your point.

It's such an amazing short story. Every time I read it I'm blown away by how much it still seems perfectly applicable.

Funny you should say that. In Sweden, to get good grades in English you have to learn lots of facts about UK, like population, name of kings and so on. What does that have to do with english? It's spoken in many other countries too. And those facts change, the answers weren't even up to date now...

> That's how background knowledge works. If you can't get over the drudgery of learning scales and chords, you'll never learn music.

Tell that to drummers

> they could just offload to a pitching machine

Sure, but watch out for the game with a pitching machine, a hitting machine, and a running machine.

I do think there is a good analogy here - if you're making an app for an idea that you find important, all of the LLM help makes sense. You're trying to do a creative thing and you need help in certain parts.

> You always could have paid or bullied the smarter kids into doing the work for you.

Don't overlook the ease of access as being a major contributor. Paying $20/month to have all of your work done is still going to prevent students from using it. Paying $200/month would for sure bring the numbers of student users near to zero. When it's free you'll see more people using it. Just like anything else.

Totally agree with your main points.

As a drummer, you need to learn your scales and chords. It still matters, and the way you interact with the music should be consistent with how the chords change, and where the melody is within the scale.

Your drumming will be "melodic" if you do so

The five paragraph thesis format isn't about learning to learn, it's about learning how to format ideas.

Just learning a thing doesn't mean you can communicate it

Not mention tuned drums

That’s odd. Outside of reading comprehension assignments I never had any fact memorization as part of any language course.

Perhaps they changed the curriculum since the 90s

Yes, I was very confused when daughter came home with some bad scores on a test and couldn't understand what she meant. I had to call the teacher to get an explanation that it wasn't history lesson, it was english lesson... Really weird is just not covering it.

Swedish schools gets a makeover every time we change government. It's one of those things they just have to "fix" when they get to power.

It would be interesting to test adults with the same tests that students were given. Plus some more esoteric knowledge. What they learned at school could then be compared to see new information that they learned after school... as well as information, skills that they didn't use after school. It may help focus learning on useful skills knowledge that people have learned... as well as information that they didn't learn in school that would be useful for them!

Actually, it shows the real problem about education... and what education is for!

Education is not a way to memorize a lot of knowledge, but a way to train your brain to recognize patterns and to learn. Obviously you need some knowledges too, but you generally dont need to be an expert, only to have "basic" knowledges.

Studying different domains allow to learn some different knowledges but also to learn new way of thinking.

For example : geography allows you to understand geopolitic and often sociology and history. And urban city design. And war strategy. And architecture...

So, when students are using LLM (and it's worst for children), they're missing on training their brain (yes... they get dumber) and learning basic human knowledge (so more prone to any fake news, even the most obvious)

> How can a human be a facilitator and shepherd of the workflows in such a way that can complement the model and grow the human?

Humans must use what the AI doesn't have - physicality. We have hands and feet, we can do things in the world. AI just responds to our prompts from the cloud. So the human will have to test ideas in reality, to validate, do experiments. AI can ideate, we need to use our superior access and life-long context to help it keep on the right track.

We also have another unique quality - we can be punished, we are accountable. AI cannot be meaningfully punished for wrongdoing, what can you do to an algorithm? But a human can assume responsibility for an AI in critical scenarios. When there is a lot of value at stake we need someone who can be accountable for the outcome.