A Student’s View on the Challenges and Possibilities of AI in Education

Michael, you've just spent a week at the happiest place on Earth. And I must admit, I'm a little bit jealous.

For those who may be confused about what the happiest place on Earth is, it's Disney World. We, I was just there with my kids, first time for them. It was, it was a blast. Diane, I, you know what I mean, I came away with a few takeaways, but one of them was the excellence at scale that Disney has 74,000 employees in that park. And like almost every single one of them, it's probably like 73,500 of them are just dedicated to making your experience better than the last person you just interacted with. And it's, it's, it's astounding. However, they have managed to do that. So it was a blast. Thank you for asking. But we're not here to talk about my vacation. Although that might be fun. Instead, we're looking to continue to dive into some of these sticky questions around K12 education. Help people see different ways through what has often been pitted as zero sum battles between the adults in the room, and try to think through how we can unleash student progress, really, and prepare them for the world into which they're entering. And obviously a question that exploded into both of our minds starting last year. Diane was the topic of, of AI and as opposed to the metaverse, it is still the topic du jour, it is still what everyone is wondering about artificial intelligence, what do we do with it and so forth. And you have been teasing me that you have the perfect guest to help us think about this in some novel ways. So take it, take it from here. Diane.

Well, I have indeed been doing that. You're right, AI so far has a longer shelf life. So we'll see how, how long that lasts. It's, it's my great pleasure to introduce you to Irham. And Irham and I first met a few years ago when he was a freshman at Minerva University. He was coming from Bangladesh to that global university. He's now a senior. He spent the last two summers as an intern at Google X here, just about a mile away from where I live. And he, at Google X, he's really been focused on large language model, AKA AI research. And you've been hearing about IR him from me and all of our conversations we've been having for quite some time. Michael. So what you know is that I've learned a ton from him about AI. And one of the things I love about talking AI with Irham is that even Though he has a ton of knowledge and for example, he writes a popular technical blog about AI that I have looked at and I can't even decipher a sentence of it. So highly technical, deep knowledge. But he also is a systems thinker and he cares deeply about how technology is used, how AI is used, and what it means for our society. And so he's willing to and able to talk with people like me, lay people like me, and help me understand that and engage in a good conversation for our purposes. I think most importantly, your home is 20 and it's so critical to be in dialogue with people in this generation. I think we give a lot of lip service and education to, you know, more of the consumers, if you will, or the students. And then we don't involve them in our dialogue. And so I'm just really grateful that he's here and you all get to meet and so welcome.

Irham, it is so good to have you here. Diane has been teasing this for a while, so thank you for joining us. Before we dive into the AI topic itself, I would just love to hear through your words because I've heard it a little bit through Diane's, but I'd love to hear it and the audience would love to hear just your journey to Minerva University, your journey to diving into topics of AI and really how has that school experience specifically been what has worked, what hasn't.

Yeah, thank you so much for having me.

I'm like, so excited.

So, yeah, let's just get into it. I'm one of those people who the standard school system was just not designed for, rather frankly. Like, I grew up inside the national

school system back in Bangladesh up to

fourth grade, essentially, and it just wasn't designed for someone like me in mind. Like, we're talking in like an institution

where, like, what we're talking 50 classrooms, teachers barely able to like, give anyone attention. And like the school system just geared to like, make you pass a university entrance exam. And like, if you do that, you've done it, like you've succeeded.

And I, my mindset often was like, I joked that like, I was learning

full time and going to school on the side. So I saw it in my mind.

That's amazing.

And I'd say the only reason my

path worked out the way it did is because when I was in finishing

up middle school, entering high school, so

like eight through ninth grades, like that window, like the Internet just like rapidly proliferated across the entire country, like within a couple months, like a short couple of months, essentially, you Went from, like, not a lot of people, you know, using the Net to a lot of people, you know, using the Internet. And I was one of those people. And I was like, oh, my God, like.

Like, it's not that I don't.

It's not that I don't like math. It's that I don't understand it. And there's a. There's a difference between those two things.

And, like, I was one of those people.

Like, Khan Academy was, like, quite literally designed for, like, I'd log on to that thing. And I was like, oh, my God, I actually understand, understand mad. And I can, like, teach myself mad. I can, like, succeed at it.

And that is the impression that it left me with, is that, like, technology is, like, really opened. Like, the Internet in particular is like,

one of those, like, frontier technologies that just opened up learning to anybody who

could access it and, like, go through the right set of tools needed to learn things.

Like Khan Academy being one of them.

And I guess that's the mindset I'm seeing this new generation of technologies in AI too. Like, I. I wonder who else is

going to be using them the way I did and learn something new that their environment wouldn't otherwise allow them to.

I suppose, like, I kept teaching myself things, and, like, that's kind of, I guess partly how I ended up at Minerva is because I wanted, like, a

not traditional, like, university education, because the traditional high school education clearly wasn't a fit for me at all.

And Minerva was like, come here. Like, we won't bore you with lectures. We, in fact, like, our professors barely

get to speak for more than five minutes in class. Like, it's all students just talking to each other and learning from each other. And I was like, sign me up.

Well, and Iram, that curiosity, I think, that you are describing in yourself is, I think, one of the. The things that led you to discover AI long before most of us discovered it. And you discovered it on the Internet. You discovered it by reading papers and sort of following these blog posts while you were teaching yourself. And so I don't think it was a surprise for you when it burst onto the scene because you, you knew what was coming and where it was coming from. And yet I think that your. The world's reaction has been interesting. And so you sent me a quote the other day. You texted me a quote about AI that had us both laughing pretty hysterically, probably because it feels very true. And so I'm going to share that quote, which is, artificial intelligence is like teenage sex. Everyone talks about it. Nobody really knows how to do it. Everyone thinks everyone else is doing it, so everyone claims they are doing it. And so let's. Let's just start by figuring out, you know, what people are actually doing, if anything. You know, can you just. This is sort of a ridiculous question, but can you just explain AI to us, like, and. And why it's suddenly this big deal that it feels like it just spontaneously arrived in 2023?

I guess there's, like, three big terms

that we should go over when we say AI because it just means so

many things, like, AI is, like, such a big, hard thing to define. But I guess the way I see it is like, anything that's just right beyond the edge of what's computationally possible right now, because once it becomes possible,

people kind of stop thinking about it as AI.

Ah, interesting.

Okay. Other people would have their own definition, but I feel like that's, like, what's historically been true. Like, once something becomes possible, it almost

feels like it's no longer AI. Got it.

What has been more recent, though, is how we keep pushing the frontier.

Like, if it was the 90s, for example, when Kasparov played with Deep Blue or the Just Playing engine, that thing was a massive, highly sophisticated program with millions of logical rules.

That's what's different. What happened over the last 10 years

is we really pivoted towards machine learning and specifically a branch of it called

deep learning, which allowed us to use really simple algorithms, like, on enormous amounts of data. Like, we're talking several thousand lifetimes worth

of data, when you're talking about training

a language model, for example, and ginormous amounts of compute to push all that data through a very simple algorithm. And that's what changed in that, it turns out really simple algorithms work extremely well when you have a large enough compute budget and enough data to pass through them. And the instantiation that, like, everyone really captured the imagination is against A narrower part of machine learning still is called large language models. The large being, like, they're much larger

than models for historically, language models, in

the sense that at their core, you give them a piece of text, and they just play a guessing game. We're like, okay, so what's the next word?

And if you keep predicting the next word over and over, you form entire sentences, paragraphs, entire documents that way.

So it sounds like, basically, I'm going to bring it back to education really quickly, Michael, like AI as you're describing it, Irham, is this concept we have in education and learning. It sounds like, to me, which is like I plus one. So it's like where you are just a little bit harder than what you can do. And that's like your, your, your zone of proximal development, like where you're best learning. So it sounds like AI is like, in that space, like it's like what we can do plus a little bit more is like what keeps pushing us forward, basically, on everything that's written on the Internet with, with simple formulas.

Yeah.

And I guess what captured the imagination is, I think, language in particular, because ChatGPT came out sometime last December, I

think, and that again, took everybody by surprise, essentially.

But the thing is, image generation models like Dall E2 and stable diffusion were

out earlier that summer, and they're built

on basically the same technology, given a

ginormous amount of data, produce more samples that look like something that came from the data distribution.

But, and I had this with friends, especially outside of computer science, where I show them these generated images and they're like, yeah, I thought computers could already do that.

What's special about them?

And I'm like, oh, my God, it's like generating like a panda in a spacesuit on Mars. And they're like, yeah, I thought Photoshop already does that.

It's so interesting.

Whereas with language, like, language feels like language, I would almost say, like, people see a conversational interface and almost by de facto assign intelligent attributes to it. Now, this is not to say that it's all smoke and mirror. It's like, these are remarkably practical technologies

that I genuinely think are going to change a lot of what we do today, as we'll explain.

But at their core, I think there's at least part of the fact that

humans genuinely see language differently than other modalities, because in part, it is unique to us that we know of, at least.

It's really interesting, though, because the implication of that, it seems, is that part of this isn't just the power of what's been built, but also our reaction to what's been built. And I think then the corollary, something Diane and I have talked a lot about, is like, right now you're seeing very polar opposite reactions to AI. Either it's the utopian thing that's going to bring about this glorious future, where resources are not scarce and everyone will be fine and like, etc. Etc. Or it's very dystopian, right? And you see a lot of the. I'm going to insert my own belief for a moment here, but like a lot of the technology leaders that have developed this being like, it could be dystopian, it could take over the world. As I hear you describing it, it doesn't quite sound like either of those is right. It's like the next step. So I, I, I, I'd love you to put this in a human context then, of like, what does it mean about the roles, particularly as we think about the future of work, that humans will play with AI, or, you know, how human AI maybe is itself or isn't. Like, how, how do you think about the intersection with humanity?

When I think about the future, one tool I borrow from a former colleague,

Nick Foster, who was like, I think

head of design at Google X, he had this. He would introduce this concept of thinking about the futures not as a thing you're approaching, but almost like a cone of many possible outcomes. There is the probable, which is the current set of outcomes that seem really probable, but the probable is not the possible.

The possible set of features is much larger.

Right now. I think we run a real risk of kind of just like, not seeing the technology for what it is. It's a tool that we could actively

shape into a future we want.

And instead it's like, instead of trying to imagine it through that lens, we're almost like we're kind of giving up and we're like, yeah, it's gonna take over the world or something. It's gonna be bad.

Oh, well.

It's almost like we're sleepwalking towards that

outcome to a degree.

And I see it genuinely as like a technology, a tool. And, yeah, like, it's up to us

to decide what integration of that into our society looks like. But, like, it's.

Do you think part of that is because some of the people behind the coding are also surprised by the outcomes that it produces? And, like, gee, I didn't know it could do that. And so they're sort of showing to your point about this, like, weird passivity that we all seem to be displaying, that maybe it's because they too seem surprised by some of its capabilities. And so that is surprising. The lay people like me and Diane, and not to mention the people who aren't even playing with it yet.

So I think it's important, like, what we've seen over the last 10 years, but really the last four, I would

say with like, scaling really taking off,

is when models get larger, they have more capabilities. Like a model from three years ago?

No, I'd say a little bond enough,

maybe four or five. If you go to the joke and ask it to explain it, it Wouldn't be able to do that.

It would, like, struggle. But if you ask ChatGPT now, like,

hey, here's a joke that my teenage son wrote.

I don't get it. Can you explain it to me?

It's gonna do a pretty decent job at it. So one of the things that came with scale is like, capabilities emerge. But the thing that surprises researchers is like, I think I saw this analogy on a paper by Sam Bauman, but it's almost like buying a mystery box. I think you buy a larger box, there's going to be some kind of new capabilities in there, but we don't know what they're going to be until

we open up that box.

I guess that's where a lot of

surprise, even from researchers come from.

That said, I will push back on that a little in that there has been really genuine and serious work done in the last two years where we're really trying to figure out, like, what data, like, what way. We're like throwing the whole Internet at this thing.

There's actually a lot of things going on in there and that these capabilities are not actually as surprising as we think they are.

Like, joke explanation.

I wanted, at the start, when it came out, we're like, oh my God, this thing can explain jokes.

But then when you dug into the data deep enough, you're like, there's like tons of websites on the Internet that

exists for you to like, dumb down a joke and explain how it works

so it didn't appear out of completely nowhere. The fact it works on new jokes that are hopefully original and it's still

capable of explaining them is still cool.

But it didn't come out of absolutely nowhere.

Yerham, you just started giving us some timeframes and timelines and you're sort of in your mind calculating 10 years, 4 years, 2 years. I just want to note 10 years you were 10 years old. But. Okay, we'll set that aside for a moment. But as you were using those timelines, one of the things that comes up a lot for people is this feels like it's going so, so fast. Like if you didn't even understand what was happening in this world and then suddenly Chat GPT came on the scene. You probably didn't look at it in December because you were busy, but then in the new year, suddenly you like, the whole world's talking about chat GPT and you log on. And then it just seems like every day something new is coming and faster and. And I talk to people who just are like, I, I can't Keep up. It's only been like a couple months and I don't even. It feels like it's just spinning so fast and beyond our control. How do you, is that true? Like how do you think about that timing and how, how do you think about keeping up? Like what, how, how can we like conceptualize that especially as educators?

Well, for what it's worth, even researchers have trouble keeping up these days with

the sheer amount of papers coming out left, right and center. It's. It's hard.

That said, and I'll say it is genuinely surprising, the pace chatgpt in particular took off because these models had existed. The model are building off of GPT3 that had existed since 2020.

We're talking around the pandemic start period.

They just put a chat interface on top of it and that really seemed to take off.

I remember reading news articles for even OpenAI seemed surprised at just putting on a chat interface on top of a technology that had lying around for three years cause it to really take off that way.

And even I was surprised because again

I was in Taipei in the spring and like Taiwan in the spring.

And I remember being on like the Taiwan high speed rail and I'm seeing

someone else use chat GPT on the train next to me and I was

like, wow, this thing is taking up

a lot faster than I realized.

But it's important to like understand that when I say again that three year,

that GPT3 had been lying around for three years before they put a chat interface on top of it.

And this is not, again, this should not underscore the fact that these models are going to get larger, probably more capable. But it should also ground you two things in that one, the specific burst

of innovation we saw in this year

in particular had been building up for a bit.

Essentially it's almost like a pressure valve went off when they put a chat interface on top of it.

The other thing, and this is the thing that I wish people would discuss more often, is that it's not just

that the models got larger and we trained them on more of the Internet.

It's also that we start paying a lot of money to get a lot of humans to label a lot of

this data so that you could fine tune what the behavior of these models are.

You see, when GPT3 was trained about 2020 or so, it's what we call a base model. It does exactly one thing. You give it a piece of text and it produces a completion similar to the text data it saw when it

was being trained, which would be raw Internet data.

And it has a tendency to go

off the rails because the Internet is full of people who say not very nice things to each other.

What changed was the sheer amount of human data collection that went in during that time frame and that this large model was adjusted over to tame its behavior, teach its skills, such as what

explaining a joke even is and all those things.

We can talk about that a bit more. But the big connection being that the jump we found those three years is that of a technology that had already existed, that we really learned to like adjust better. But we already burned through that innovation once.

Like we like, we like it.

Like, it's unlikely we're going to see like another leap on the same scale

of like learning how to use like

supervised, like human fine tuning again because

like that innovation has now already exist and is already baked into.

That's fascinating. It's something I hadn't understood before either, which is to say that in essence, if I understand you right, obviously the code base continues to evolve where, you know, GPT4 and so forth. But in effect, I think what you're saying is like the user interface and how we interact with it, the right. Is what actually changed like the skin of how humans interact with the code base. I think it leads us to where, you know, we, we love to talk on this podcast, which is the uses in education and how it's going to impact that. And obviously I'll give you the hall pass if you will. You're not an educator, so, you know, we're not asking you to opine in, in some way that puts you in a position you're not. But, but you are a student right now at a student at a cutting edge university, constantly thinking about pedagogy. And so I'm, I'm curious from the student perspective, what excites you at the moment about AI in education?

I think shrinking the learning feedback loop

is the way I would put it. I'm a systems thinker and like I use the lens of feedback loop a lot.

And whenever you shrink a feedback loop from say learning something, like maybe getting a feed, like what I say by feedback loop in education is like, you

write a paper, your professor takes maybe two weeks to get it back to you.

And you got, you get like, almost like a signal, like, hey, you got these things right.

You don't get these things quite right though.

What happens when you shrink that from two weeks to a couple minutes or

maybe a couple seconds?

It's not just shrinking a number, it's changing how you interact how your learning experience evolves. I think it's nice to connect this back to my middle school years. I think the reason I ended up in math and programming in particular is because those two things have really short feedback loop.

In programming in particular, if you write bad code, your compiler just screams that. It's like, hey, this thing isn't. You're trying to add a number to a set of words, it's not going to work.

And you get really short, tight feedback loops to keep trying your code over

and over again until it succeeds.

That's not true with learning English. With learning English, you write a paper,

you wait a week for your professor or teacher to grade it back to you.

You may be pick up something and

like, try again next time.

And I would say at least part of the reason I ended up picking

math and like mad programming is again, like, I didn't have that much great resources in terms of, like, teaching, like, teachers who could, like, really help me out. So I naturally gravitated towards things that, like, I would be able to like really quickly iterate on math and programming

were those things English, even the sciences,

even, I would argue broadly, are not on those same lines.

Like, but what then changes with AI then is like, you now have a chatbot that it doesn't have to be a chatbot.

It could be far more than that, really.

You have a computational tool that can, like, actively critique your writing as you're writing it out. Like, you're like, hey, like, what are some ways I could have done this better? They're like, yeah, you're like, using these passive. You're like, you're using these passive wordy phrases.

Maybe you shouldn't be doing that.

And they're like, why shouldn't I be doing that?

Because, like, because it makes it harder for other people to read.

And instead of waiting a week for

that to happen, you get that feedback in real time and you have another iteration ready and you then ask it again, hey, how would I do it? Even better. Like, that loop shrinks significantly.

That's fascinating. And I think it's also what we, Michael and I have been working on, personalized learning, or whatever you want to call it, for a decade plus at this point. And that is certainly one of the promises of personalized learning is that tight feedback loop. So you're staying in the learning. You know, learning isn't delayed and it's contextualized and it's in the moment and immediate. It's more tutor, like, if you will. That's why so Many people have put so much energy into that. And you have now said a couple of things. One chat GPT putting this, like, skin on their product, which is essentially a chat bot. Like, you talk with this bot or this whatever window. And then also this potential of what you just described, like the feedback coming from sort of a chat bot, if you will. And in my experience, most people, when they think about the uses of AI, are thinking along these lines. Like, that is what they think is there's going to be someone, whether it's a little avatar or a box or something, that's like sort of chatting with me, and that's kind of how AI is going to play out. I know that you sort of get a little exasperated when that's that, you know, all people can imagine what else, what else might be possible, help us expand our thinking a little bit beyond just this sort of chatting.

I think right now we're at the phase when the iPhone would have been sometime in 2007. I'm not really qualified to comment on that because I would have been what?

Don't worry about it. Don't worry about it.

But at least from my understanding of that time period, apps were a new thing. Like, people didn't really know how to fully utilize them. And, like, the first set of apps were kind of like window gag. And I was like, people were building those, like a flashlight app where instead of turning on your phone's flashlight, just

showed a flashlight on the screen.

I downloaded one of those. So it's true.

So I feel like we're in that

era for these language processing technologies right

now in that we have a brand new tool. We're not entirely sure what using that looks like. And returning back to the quote Diane quoted with the Enterprise, I feel like not nearly as many people are asking, how does this help us solve our problems better? And a lot more people are asking, how do I put this into my product? So my board of directors is happy.

Yeah. So the idea being, like, what legitimate problems do I have and can I use this to solve them? And that's where the what's a useful app, not flashlight is gonna come from.

With Chatbots in particular, Chatbots became the first big use case. So everyone's like, well, this seems to work. Let's make my own Chatbot.

But, like, slightly different.

And I think it's just so unimaginative. But the other thing with Chatbots is that they have really bad what we call affordances in design. And affordance is something that, like, almost like cues you into how to use

something like when you see a handle on a door, you're like, oh, this

is a thing I grabbed.

Right.

Like Dan, let me ask you, like if you had to choose between a big cancel button that like cancels your

flight that you don't want to go

on versus dealing with a chatbot to

cancel your flight, which one would you pick?

Yes. And you have showed me this and demonstrated this to me. Like a single button like that is so much more useful if you know the thing that I'm going to do versus making me talk to it and explain it, where something's inevitably going to go wrong. Right.

I mean these technologies are so nascent right now and what people really need to appreciate is that you need to design them in ways where you almost expect they're going to produce some unrelated

unpleasant output somewhere along the process.

So it's almost like a liability if

you're giving a user an open box to type anything they possibly want.

And it's not just a liability, it's also just makes the user again, it doesn't give the user any affordances. They see a blank box again.

If you need an English tutor bot,

you could just as easily have a couple buttons that could summarize, remove jargon, sorry, highlight jargon or introduce a couple buttons that cover a couple use cases that a standard 9th through 12th grader

may want to use so that they can refine their writing better.

Don't give them an open ended box where they don't even know what to

ask for help in.

And would that still be using AI those buttons?

I mean behind the scenes. At the end of the day, any implementation of these things that you've looked at is a roleplay engine. If you have interacted with one of

these airline processing, like if they use a language, they're using large language model

behind the scenes, there's like there is only two bits that anyone else modifies.

Once you have the model, which is

you have a dialogue preamble, which is almost like a script someone else writes. As you are now about to role play a dialogue, you are about to

assist a user with their airline queries,

then the actual dialog that happens, all people do when they're creating a new

implementation of them is that they switch out the preamble with something else.

I'm just saying you can write so many different, you can write a preamble

for each of those buttons.

Essentially the button is like, hey, like you highlight jargon. Then you're like, okay, you are about to role play a bot that takes in some English text and like returns the exact spans of text that are

abnormally jargony for the writing of a 9 to 12 year, like 9 to 12 grader.

You as the developer should be writing that script because you are the one who knows what this person needs to be using. You shouldn't leave it up to the

user to be writing their own script

essentially, for the most part, unless it's

like a very specific use case where you would want the user to be

writing that, but don't treat it as

the default one, which we for some reason do.

So it's super interesting, the implications on education and I have a couple takeaways here and I want to test them by you and then get your reaction. So the first one is on the flashlight app analogy. I think the implication is that if the iPhone or in this case OpenAI is ultimately going to very easily incorporate the thing that you've thought of in their own roadmap, your app is not, your sort of idea is not going to last very long on its own. Right? Like the reason there were these apps was like there was not a button to do your own flashlight or change colors or things like that. And so they built these apps and then very quickly, you know, iPhone realizes, hey, we can just add a quick little button. But I think the second implication is as you start to think about the user experience and come up with these prompts that a user might want to go through so that they're not guessing what they're putting into that open ended box that like the opportunity for educators might be to start to build around these different use cases that if I'm understanding you correctly, perhaps OpenAI is not going to develop all these different use cases and so forth, but instead the opportunity might be for educators to build on top of their existing, on top of their code to develop these sorts of things that help get the problems solved that they and their students are actually trying to solve. But I'm curious, like where that line ends and if you see it differently,

I mean, it's not a clear hard line. I mean it's, I mean, what's on OpenAI's rate map?

I mean, I can't really like, I

don't know, but I guess it ties back into like what your company is. Like if your company is like trying

to add value to people's lives, like

that really means sitting down as like, oh, am I just going to write a wrapper that like takes in a

PDF and like allows you to just chat with it?

I mean, those all kind of like

went the way of the dinosaurs last week when OpenAI just integrated like, hey, you can drop a chat PDF to our thing.

But if you really try to aim for deep integration like OpenAI has, I

mean, I'm making claims here, I don't know for sure, but like, I would

imagine they don't have a deep understanding

of the K12 education system, nor would

they be necessarily interested in that because that doesn't seem to be what their

goal right now is, which is to make bigger and better version of these models that are more generally capable. What they're counting on is that other people take these models and like integrate them throughout the rest of the economy.

And that's where other people come in

and that's where K12 educators come in. Because like they are the ones who have a better understanding of like what these buttons need to be, what, what they want their students to be learning at the end of the day.

Love it.

What's coming to me, Michael, is, you know, on our last episode we talked with Todd Rose, who wrote the End of Average. That is in my mind, the foundation of a lot of personalized learning and one of I think the misconceptions people have or the swings. Education loves to swing the pendulum, you know, as we go from like totally teacher directed, controlling every second of every bit of learning and we swing all the way over to like basically go teach yourself, we're just gonna throw you out in the wild. And at some level, just throwing people into a chat bot of a large language model is throwing them into the wild. And so what I hear you saying, Irham, is we need the in between. Like there's a real role for educators to narrow. It's not like the whole world. We can, you know, personalization is there's two, three, four, five ways of doing something and we can narrow down to that and create a much more personalized experience that is curated by expert educators. And we should be looking for that happy in between.

I mean, again, like, it's been so fascinating seeing these chat bots interact with the education system and like kind of freak havoc, honestly, like to a degree where professors like taking stances on everything from like, we want we should go back to making everyone write things by hand.

By the way, please don't do that.

Like they're an opportunity. And it's, and here's the thing, this factory model of the education system we

have where people just like go through it, do these problems, hopefully learn something by doing set problems. We don't need to know they're learning the thing. We just need to know if they're doing the thing.

Exit and da da. They have like a high school diploma. Like, that was going to come to an end because that's already been out. Like, that's already ill preparing people for

the jobs of today.

For a while now, these models, they're not bringing about something that wasn't going to happen.

They've just sped it up.

Essentially because students already again, ask why a student would actually go out of their way to get an essay written by these things if they genuinely believed in their own education that, hey, if I do this thing, I learned something

new and that will be helpful in the future.

They probably wouldn't. So why do they feel disillusioned? Because they know deep inside that what

they're learning in their high school is not going to actually prepare them for the world.

And you need to actually deal with that disillusionment. And on the counter, I think these models provide an excellent way to actually start tackling that disillusionment by educators seeing themselves almost as designers, as what people need to be learning. I use the example of the door handle because it seems like a simple object. It really isn't. If you've ever been in a hotel with like a. One of those weird, poorly designed shower knobs, you know how much bad design

can mess up your day.

And when good design works, it's almost invisible.

Like, you don't even notice a shower knob when it actually works.

And I think that's what good educational

software using these will almost look like.

The students won't even realize how seamless it feels.

Like they press a button that like, tells them, hey, this is the jargon you're using. Here's why it's bad for you. Here's an explanation how you could be doing better this time. It should feel seamless and they should

feel less disillusioned because they feel like,

oh my God, I'm actually learning something here.

So I want to stay just as we wrap up here. And one last question before we go to our sort of bonus round, if you will, of stuff outside of education, which is you just painted a good picture, I think, of how the education system has reacted in very nervous, let's call it ways to this advent of it because it is immediately sort of thrown into question so many of these tired practices that it holds on to. And I guess the corollary question I'm curious about, I've heard a lot of students, Let me, let me frame This a little bit more. I've heard a lot of students say, you know, Professor X, like you're thinking a lot about what is the assignment and how am I going to catch you from cheating. But you're spending a lot less time thinking about what do I need to learn to be prepared for this world in which AI is going to be underpinning basically everything I could possibly go do in a career. And so I guess the question, I'm curious from your perspective is as you look at these traditional factory model education systems, what, what's something that they should start teaching students that they don't perhaps today? And what's something maybe that they should lose that they continue to hold on to?

I mean, honestly, I don't want to sound like a shill from Minerva, but I am going to, I think freshman year, like I had never written a full length essay prior to freshman year in English and I was like kind

of really lost, honestly.

But one thing that really stood out is like my professor spent so much time like just breaking down the act of writing into like, what does it mean, like to have a thesis, what does it mean for a thesis to

be arguable and substantive instead of something everybody universally agrees with? Because if everybody agrees with what you're writing, you don't need to write it.

Like really breaking down the act of writing into these atomic skills that like

I keep finding myself using even at the tail end of college now in

senior year, I think that is the kind of thing we're going to need to do. It's like actually asking ourselves like, this is an instrument, a tool we've built,

that we administer to our students in the hopes that they'll learn something.

Does this tool actually do the thing it advertises? It does, because a lot of the time it just doesn't. And we just kind of need to be honest about that because otherwise. Yeah, because like think we've been like, again, like it's, it's a lot like again the chat GPT moment in some sense, but also for education. It's been building up like a pressure valve. And that pressure valve kind of just

went off in the last year.

Wow. Well, we could talk for a long time, but that might be the place to land it today. But before we let you go, we always like to at the end just mine for, for what we're reading, watching, listening to, outside of our day to day work. And so we're curious, do you have

any time for that as a student? Also,

can I talk about a video

yeah, yeah, that's great.

I've been playing a lot of Super

Mario Wonder, which is like the New Mario Brothers game from Nintendo.

It is fun. It is really the best way to describe it. Like, a lot of media, like really like, enjoys like you know, being dark

and gritty and mature or whatever.

Nintendo's like, we're gonna make a game that's like unashamedly fun and bright and colorful and just, just like playful and like they've been doing that for the better part of like, I don't even

know, like 30, 40 years now.

And they've kind of just like stuck to it as a core principle. And like, I kind of just admire

like their ability to like, really set

up mission for themselves, which is make

things that make people like joyful and fun and actually just stick to it for like the better part of half century.

Oh, I love that one. Diane, what about you?

Well, we're gonna look kind of boring following that one. I'm. I'm gonna go to the dark, gritty world. I just. I just finished reading the book How Civil Wars Start and How to Stop Them by Barbara F. Walter. I will just say seven of the eight chapters do an excellent job of diagnosing the problem and it's pretty terrifying and I do think we should know it. Chapter eight, where the solutions come in, was not compelling to me and so it feels like there's work for us to do.

And I guess I will say I'm in a similarly dark place, maybe Diane, because I read the Art of War by Sun Tzu. So go figure. Everyone can figure out what where. Everyone can figure out where our headspace is. But I finished it before Disney. I had tried to read it a couple times before and this time I made it through, which is setting me up now for reading clause of its, which is where I am now. Grinding through is the right verb, I think. But on that note, Earham, thank you so much for joining us and making what's a fraught topic, but a topic with a lot of hyperventilation. Thanks for making it really accessible and exciting and giving us a window into where this could be going. Really, really appreciate you being here with us on class disrupted. And for all of those listening, thank you so much for joining us. We'll see you next time.