EP 316: The Future of Generative AI in the Classroom. How Will It Work?

This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. How will the future of higher education work when AI seems to be disrupting everything? Right. I think so much of what we talk about here on the Everyday AI Podcast is what happens in the business world. But there's something very important that happens before that is our future leaders of tomorrow. The future people running our businesses are learning and they're growing and they're getting trained in colleges. But not all colleges and universities are on the same playing field when it comes to generative AI. So I think today's conversation about the future of generative AI in the classroom and how it'll actually work is a very important conversation to have. All right, I'm excited for today's episode. What's going on, y'? All? My name is Jordan Wilson and I'm the host of Everyday AI. The this show is for you. This is a daily live stream podcast, free daily newsletter helping us all learn generative AI so we can leverage it to grow our companies. All right, so if that's you, if you're listening on the podcast, thank you for joining us. Make sure to check out your show notes and go to your everyday AI.com in today's free daily newsletter. As we do every single day, we'll be recapping the episode with a lot more information that you need, as well as everything you need to stay up to date in the world of AI. Before we get into today's conversation on the future of generative AI in the classroom, we're going to let's kick it off as we do every single day by recapping what's happening in the world of AI news. So first, we were able to slip this into our newsletter yesterday, but it came after the podcast. It's worth mentioning. Well, some tech giants, according to reports, were secretly using YouTube subtitles to train AI models. So tech companies are faking are facing backlash for using YouTube titles subtitles without creators consent to train their AI models, raising ethical and legal questions. So according to reports, subtitles from nearly 200,000 YouTube videos across nearly 50,000 channels were used by companies such as Anthropic, Nvidia, Apple Salesforce, and many others. So the data set was named YouTube Subtitles and it includes transcripts from educational channels such as Khan Academy, MIT Harvard, as well as popular shows like the Late show with Stephen Colbert and many famous creators. So Google, the owner of YouTube, obviously claims that its use of videos for AI training is permitted under agreements with creators, though OpenAI and other big tech companies have neither confirmed or denied similar practices. And there's also some high profile YouTubers including Mr. Beast Marques Brownlee Pew Pew die reportedly had hundreds of their videos subtitles harvested without their consent or permission. So we'll have more on this in the newsletter as well. So our next piece of AI news, Google Labs started teasing its next version of its AI image generator Imagine three. So there is no release date yet, but Google has for the first time started to consistently share generations created with their new AI image generator. So it's very infamously, I guess Google's generative AI tool Gemini for faced backlash for generated contra for generating controversial images that depicted as an example people of color in historically inaccurate roles leading to its temporary suspension. So that was its old image gener imagine2. They fixed it, relaunched it. So we'll see what happens with Imagine 3. But from the samples which we'll be sharing in our newsletter, it does look much better than as an example Dall E3 from OpenAI. But it does look obviously well behind leaders like Mid Journey. All right, our last piece of AI news for today. An OpenAI co founder has launched an educational lab to help people better learn AI. Speaking of today's episode. Right, so Andre Karpathy, a prominent AI researcher and a co founder in OpenAI, has just announced the launch of Eureka Labs, an AI and education company. So Eureka Labs aims to create an AI native learning environment by integrative, by integrating generative AI with traditional teaching methods. So Kathy described the venture as, quote, a new kind of school that is AI native and emphasized the scarcity of passionate and fluent subject matter experts. So yeah, we'll have more information on that in today's newsletter, so make sure to go to your everydayai.com and check that out. Yeah, interesting, because I've been saying this for like a year and a half, like there needs to be more people focused on education in AI. And a lot of times when these, you know, co founders of big companies like OpenAI and others leave, they leave to start another large language model company or, you know, an AI consulting firm. So this is the first time we've seen someone really focus on AI education. So speaking of education, that's what we're here for today. I am personally very excited for today's show to talk about the future of generative AI in the classroom and how it will work. So please help me. Welcome to the show. There we go. We have Him, Jules White, who is the, let me get the correct title, the senior advisor to the Chancellor on Generative AI at Vanderbilt University. Jules, thank you so much for joining the Everyday AI show.

Yeah, thank you for having me. And this is an exciting topic that I'm, I'm excited to discuss.

Oh man, I'm, I'm so, so excited for today's show. So, you know, for our live stream audience, make sure you get your questions in now. So Rolando joining from South Florida or Tara from Nashville. Jay, Chrissy Douglas. Everyone else, thanks for tuning in. Get your questions in now. Maybe we can get them answered. But before we get into that, Jules, can you tell everyone just a little bit about what you do there in your role at Vanderbilt?

Yeah, so I'm a professor in computer science and what I tell everybody is November 1st of 2022, if you'd stop me on the street and you'd said this thing called ChatGPT is going to come out and here's what it's going to do, I would have told you, trust me, I will not be alive when I see that level of advance in computing. And then it came out and so I've spent my time thinking about how do we teach people to really use, use this and innovate with it. So I created a prompt engineering course that's on Coursera that has almost 300,000 people. And then within Vanderbilt, I lead a lot of the university wide initiatives on generative AI and how do we incorporate it both into our operations, but also our education.

And let's just start at the end. Sometimes I drag people on, right? And you already got to listen through the AI news to get to the good stuff right here with Jules. But let's just skip straight to the end. How is this going to work, Jules? Because I feel that AI, and especially in higher education and so polarizing, either colleges and universities are going all in and doing a fantastic job, or they're still banning it, not teaching it, not allowing students to use it. So how is this ultimately going to pan out?

Oh well, there's no question how it's going to pan out. It's going to pan out that we realize that it's an incredible tool to teach students how to use it to support their learning and to create all kinds of completely new learning experiences. So there's no question that people are going to get past the perspective of it's a tool for cheating and they're going to learn how to teach people how to use it effectively. And a simple example of this Is like people by default go in and use it and they don't exploit the generative aspect of it. That is they don't get options. So a simple way that a student can use this, they can go, say, give me five different ways to solve this problem and compare and contrast them. And the moment that the student sees that, it's not giving them an answer anymore, it's giving them something to think about that then they have to decide, which way am I going to choose and why. Yeah.

And you know, one thing that you mentioned there is talking about new learning experiences, but I don't know. My, my point, you know, obviously as an outsider, is new learning experiences. And in traditional higher education, those two don't always overlap very well. Right. Like that's, that's my opinion. It seems like sometimes change can be very slow in higher education. What are the challenges? Right. So, you know, obviously, and we'll talk about this, I know that, you know, Vanderbilt is on the, you know, more on the innovative and pushing AI side forward, but what are those challenges for the rest of those colleges and universities in the middle on being able to adapt to those new learning experiences?

Well, I think the biggest challenge is that like the moment Chat GPT was turned on, it created the largest educational gap and need in the world, basically everybody, overnight, because it's going to affect every discipline needed to understand how do you think and solve problems with it. And basically nobody knew how to do it. And so what you have is you have a very small number of people that really understand how to go and use it effectively and how to solve problems with it, how to use it to support learning. And so the first thing that those institutions have to do is they have to close that gap and they have to get their faculty and staff up to speed on knowing how to use it themselves, but also like really understanding the techniques that are going to be helpful in learning. And then it's a very interdisciplinary thing. So this is not like the problem that you saw is a lot of places went and they said, okay, this is something our computer science or AI or data science people do. We'll let them do it and we'll, that's it. But no, this is something every discipline needs to know. Right. And so you have to start thinking about every department. And that's a really big lift for a lot of places.

Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Gen AI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead of and some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website will help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gene. I was in, you know, college 20 years ago and even at the time, like I thought like, is this the best way to learn? Right? If I'm being honest, I wasn't the most, you know, attentive student. You know, I was actually usually in class, I was working. But then, you know, we like so much, it seemed like so much of our final grade, I don't know, 80% or more was based on writing papers and I was a journalist, so that was very easy for me. Is that aspect of higher education going to change? Right, because when we talk about cheating, I think at least, okay, colleges, obviously every single student out There is using ChatGPT to write their papers, right? So is just how higher education happens going to drastically change if it hasn't already?

Well, I mean, I think it will drastically change. There's no question how it will change. We don't really know yet. And I think it's going to be, you know, completely dependent on each discipline. So like in computer science, like in my senior level class, right from the beginning, I was like, okay, everybody should be using this. I'm going to teach you how to generate high quality code with it. I'm going to teach you the issues like the fact that it can't generate something of the scale that you need. So you're going to have to think about how you design things to incorporate what it can and can't do. But it also made me dramatically raise the bar in terms of the sophistication and quality of what I was expecting them to be able to do. And so you're going to see things like that play out, but you're going to also see like, let's say in an intro class, we're going to have to figure out, and we're still working on it, like, do we still teach people to write code by hand? Do we teach people to read code? Do we teach people to write code assisted with this, like, what is what? And we don't know the answer to yet. And there's so many disciplines, you pick your discipline, there's going to be the equivalence of that. But that's what's being discovered right now. And honestly, that's the opportunity for education. That's what makes my job exciting.

Right.

Is I get to go and be part of that and figure that out.

Yeah, it's a great debate. And we've talked about that. Right. Even Nvidia CEO Jensen Huang said, ah, you know, maybe kids won't be coding in the future, maybe they shouldn't. But something that you said earlier that I want to unwrap a little bit here, Jules, is, is the importance of not just prompting, but using AI across different sectors, different fields. Right. You said, oh, early on they're like, oh, let the, you know, the cs, the computer science people or the it, you know, departments handle this. But it's not really that. Right. You've had a prompting, you know, very famous popular prompting course. We've had, you know, more than 6,000, you know, people take our live prompting course, you know, dozens of Fortune 500 companies. How important is it just for students, especially just to be able to learn how to prompt, to learn how to, you know, interface with different generative AI systems? How important is that as a skill set versus things that maybe we've traditionally been taught, you know, through the decades?

Well, I, I think that it's. I, I don't know if I can say that exactly how important it is, but I would say that I don't think that we can undervalue it. Like, I don't think that. I think it could be a foundational skill that every single student coming in should know in college. But the truth is, is like, I just launched a generative AI for kids course on Coursera yesterday. And I think the truth is, is like K through 12, we should be teaching kids from the beginning how to think about and engage with generative AI in the right way, in the ways that are going to support their learning, that they understand what it can and cannot do, but also that they start thinking about, like, when do I reach for generative AI as a tool to help me solve a problem? When do I not? And if I do reach for it, understanding those building blocks that they can put together to solve the problem. And I think it's like a foundational skill that needs to be very early. In fact, I think If K through 12, if you're still teaching coding, you're nuts. Like you should be teaching prompting 100%, way more important.

I love that. And speaking of teaching. Right. And I know it's probably easier said than said than done for both, you know, high school teachers, professors at the university level in colleges, but for those teachers and professors that can teach AI or they can use AI, where should they begin? Because I can only imagine how difficult it is to, you know, both teachers teach and encourage students to use AI, which I think is, you know, one of the most in demand skill sets that employers want, but not to be over reliant on it and to still actually learn things. How can people find that balance? How can educators find that balance?

Well, I mean, as a shameless plug, I would say the starting point is you take my prompt engineering for ChatGPT course. Once you've done that, I would say the starting point is you help people learn to use it as a tool for exploration and giving them multiple perspectives on something. This is a starting point for learning, is when you go and you can say, okay, I as a human being am biased. I'm biased about data, I'm biased about all kinds of things. So I'm going to use the tool to help me overcome that bias. So you take some data and you say, okay, give me three different conflicting perspectives on this data. And then as a human being, you have to think about them and which one do you agree with and why? And so it engages your brain, engages your thinking. I think the most basic thing is you stop asking it for answers and you start asking it for perspectives. And the moment you get multiple perspectives on a way to solve a problem, data, the email you're going to write, anything, you as a human being have to employ your own aesthetics and critical thinking to decide which one you're going to use and why. And often you'll learn from the different interpretations that you get out of it. And I think that's the starting point, is you teach them to exploit the generative capacity of it.

Let's, I want to hit this from another angle and talk a little bit about the quote unquote business of higher education. Right. Again, I think, you know, in the post Covid world, I think it made it difficult for, for teachers, for universities, for everyone. Right? You know, and having more hybrid classes and how can you still have an engaging learning Environment. But how would you say generative AI affects the future of the business side of schools? Right. You know, because I think maybe people are going to be able to get good quality education that they weren't able to get before because of generative AI. What are your thoughts on that?

Well, I think that you're always going to have a cohort of people who are really good self learners and this gives them a tool that just, you know, is going to be an incredible support to learning, like, because it fills the gap that they didn't have before, which is the ability to go and ask the question. You know, if you go to the in person, you can ask the question of the faculty member, you can get a follow up. But I think most people like they're coming to the university to learn, but also for the environment of being there with other people, to talk with faculty members who have different personalities and things. And I think you're still going to have that. But I also think that what you're going to have is like this new experience where we have all these other ways of helping people learn. Right. If you don't ask a question in class, you can go back to your room and there's a way to go and ask it and get it explained to you and personalize. And I also think about faculty members we're going to be building essentially, you know, we've got our material that we want to deliver, but it's going to be like a template, right? And when you as a student get it, you're going to know how to go and immediately personalize it for you and inject your learning style and all these things. So it's going to be more of a collaborative effort rather than me just deliver material which, you know, you must accept in its current form. You're like, okay, let's reshape this so it fits. You know what I want. You know, it's like when I tell my son, I'm like, okay, let's go have it quiz you on state capitals. And then he is like, okay, but let's inject baseball into it. So ask me about baseball teams and I have to know the state capital of the state that the team is from, you know, and things like that.

I think, I think that's a great example, right? And just the power of personalized learning. And I think back, wow, I wish I had that when, you know, I was in middle school or when I was in high school and how much easier learning would be. But do you think that maybe there is an over Reliance. Is there a risk on, you know, kind of like quote, unquote, both sides? Both, both university, you know, leaders and students using AI too much? I, I find myself asking that all the time. And there's always these memes on the Internet where, you know, someone enters three bullet points and they say, you know, make this into a long, you know, form for, for my boss. And then the boss uses AI to turn it back into three bullet points. Is there a danger in higher education of both kind of like quote, unquote, students and professors being too reliant on, on, on generative AI?

Well, well, I think absolutely yes, if they're using it the wrong way. So I think of it in terms of not artificial intelligence, but augmented intelligence that's going to help augment and amplify our critical thinking and problem solving skills. And I say it's like an exoskeleton for the mind that you play, put on. But at the same time, you don't want your mind to atrophy, so you have to use it in a way that benefits you and your learning and your thinking. So, like, that example of giving options is a great one. Like if you're going and you're saying, write the email for me, you stop thinking. You start copying and pasting that email. I see that students send emails that are like, you know, dear insert faculty member's name. And then it has this really nice thing. Will you write me a recommendation? If you say, generate five possible emails and talk about why I might send one versus the other one, then the person's learning in the process, but they're also thinking about, which one do I like and why. And you're not atrophying. And so I think the key is, is that we have to teach people to use it in a way that they don't stop thinking, but they think more about the problem they're solving, not less. And a lot of people start thinking less.

Yeah. And I obviously don't think that that's just a higher education university. That's something I think about all the time. Right. That generative AI has so much power and potential, but it also has maybe, if we're too over reliant on it, it maybe keeps us from actually learning new things and practicing our skill sets. But that's another show for another day. I want to get into some, maybe some hot take territory here, Jules, because I know no one can predict the future, but this is something I think about all the time. Right. I've been able to learn so many things over the past couple of years with the use of generative AI and obviously, you know, there's more and more even things being, you know, given or offered to kids. Right? Microsoft copilot putting GPT4O in the classroom. Same thing with OpenAI. You have Khan Academy. All these personalized learning experiences. Are colleges and universities in the future, decades in the future, is it still going to be the same? Are we still going to need all these colleges and universities, or is there going to be a certain point, you know, where in. In Andre Karpathy or, you know, we're just all getting degrees with, you know, Khan Academy? Like, what does that look like with AI?

Yeah, well, I think that, you know, like, I think about my son and I'm like, right, is somebody going to put a VR headset on him with a virtual teacher and he goes and does everything? And I think, no, he's going to go to school and he's going to get sweaty on the playground and he's going to, you know, get in trouble for, you know, messing around in line and having fun with his friends, and he's going to sit on the carpet and all the other things that come with that and go to pep rallies. And that's part of the experience. And he learns from everything. That's part of that. And he is able to, I think, to engage with the learning better because he does all that. I think if he's sitting there in front of the screen. I think university is kind of the similar way. I mean, like, there's so much that goes into it, but, you know, will we use these tools to enhance the learning experience that goes on 100,000% now, can you already go to Coursera and get a vast number of amazing, you know, courses and things? 100% you can, and you could go and give yourself a tremendous education just from your laptop, just sitting there on something like Coursera and then supporting it with generative AI. And for some people, absolutely, that will make sense, but for many people, that's not going to make sense and that richer experience is what they're looking for. So I think you're always going to have both sides to it. Now, I think where it gets more interesting is when you start thinking about things like training and compliance and certification. All those types of things, I think, are the ones that are more at risk in the near term right now.

Is there, you know, too few qualified students coming out of colleges? Right. You see all these studies about how generative AI skills over the last, especially over the last year are Exploding in demand. And it almost seems like there's not, you know, and different studies say different things, but seems like there's not enough, you know, quote unquote, kids, young adults graduating college right now to keep up for this demand and the skill set, do you see that as a problem? And if so, how can higher education keep up when we know sometimes change can be a little slow?

Well, I think, I think the answer is yes. I mean, ideally, every single student that's graduating from, I would say, high school has these basic skills and understands how to use generative AI. Because I think it's going to be a fundamental need to be effective in most jobs, like within a year or two, you're going to really need to know this. And over. And in five years, like, it's absolute necessity. Now, at the same time, right now, you can be a world expert with like a year and a half of experience in this space. Right. So I would say there's also an incentive right now to be teaching your students this, to put them way ahead of the curve earlier. But I think long term, everybody's going to need to know it and we have to engage and start teaching these fundamental skills early. I would ideally like people coming into the university who already have a lot of those fundamental skills, and then they go into their discipline like we do now, and we refine those skills in the shape of, of what's needed in their discipline. But how do we get, like, do we need more now? Yes. Is it going to take time? Yes. Partially because we haven't innovated within the disciplines yet. And I think that has to happen.

Yeah, Yeah. I think innovation and innovating quickly is so important. And I was kind of chuckling there because what you said there, Jules, is so true. If you have a year, year and a half of experience right now, you could be, you know, viewed as a, as, as a, as a leader in the field. You know, I, I always, like, scratch my head sometimes when a, you know, company doing $100 billion in revenue reaches out to us and like, hey, can you help us with prompt engineering? And I'm always like, wait, what? And then I'm like, okay, maybe, maybe it makes sense. But a couple questions that I would love to get to, and this one is kind of related with what we just said. So, Douglas, asking here, speaking of rapid innovation, how do teachers in courses keep up with rapid development? Yeah. How can you get things approved right at the university level when it can take months? And then it's like, oh, that's, you Know, that's so old now. How can that work?

Well, I think if you go back and you look at the fundamentals about like, you know, we've always done this as faculty. And I mean, in computer science, right? Everything's changing continuously in computer science. It always has been. Right. And so my job as an educator has always been to distill what's happening down into what are the core principles that are more timeless. And so when I created my prompt engineering for ChatGPT course, everybody said, well, isn't that all going to change in two months? And then the answer is, no, it didn't. Those fundamental principles and how you think about solving problems with generative AI have not changed. And we need to focus on those things. And faculty need to focus on educating them on those themselves on those things. And universities need to think about how do they take those core principles and rethink learning, rethink how they deliver learning, create content, all of those things based on those core principles and capabilities. And then from there, you know, yeah, the tools will change. Like, you'll have, you know, Claude 35, you'll have 4.0. Those things are sort of irrelevant. It's like swapping different engines in the car. But if everybody already knows how to drive and they know what a car, car is, it doesn't matter if you swap engines. It's just like you get some more performance out of it or fuel efficiency or whatever it is.

That's a great analogy. Yes. Swapping the engine out. Another great question here from Tara. So thanks for joining in saying, have you seen faculty shifting their assessments toward things AI can't do? If so, what creative ways do you endorse?

Yeah, that's, that's a great question. So the answer is yes. I mean, I've seen faculty shifting their, their assessments in all kinds of directions. I mean, one is that just faculty expecting so much more out of students saying that, like, my goal at the end of the day is to help you be able to build and create amazing things. I'm just going to scale up the size of what I'm going to look for, but also the quality of what I'm going to expect you to be able to do and have you be able to integrate all these different parts together. Because the moment you scale up, what you tend to have to do is you have to think about how do you integrate and make all these things cohesive. And so that's one area that I've seen a lot across disciplines. I've also seen faculty get really creative with it. So there's a faculty member in French at Vanderbilt, and she was having students generate dialogues between historical French figures, and then they would have to go and discuss the accuracy of the language for that historical time period and what was happening. And, like, did it get it right and how. And are there subtle issues in the language? And. And, like, that requires students to really engage. It's fun because it's creative and interesting, but it also requires them to creatively engage with it. And then I've also seen, you know, people just go in and say, well, okay, we're going to, you know, talk about things in class and other things, and we're going to bring discussions from class into assessments. And now if the student wasn't there in class, engaged in listening, they're not going to have the appropriate context to even feed into the generative AI to begin to answer the question. So there's all kinds of creative ways to go about it. But I think at the end of the day, our goal should be to figure out how do we use these tools to make students think even more deeply, you know, rather than worry about, like, are they cheating? I mean, like, there's a Stanford study that came out that basically said, we monitored cheating before ChatGPT. We've been monitoring after ChatGPT. There hasn't been a change just the way that students who do want to cheat decide to cheat. And I think it's also a little bit of disservice because, like, people pretend like, all students are cheating, then they're not.

I'm going to open myself up for one here, but since we're talking about it, I have hot takes on this, so I want to know yours. Should universities be using these AI detectors? It seems like that's the. The crutch. You know, so many universities have been using over the last, like, two, two to three years. Should they be using those? Are Those actually real?

100. My perspective, my. My personal opinion is 100% no. I think there's snake oil. And when you have OpenAI produced a detector and then they took it off the market because they said, hey, it's not. It doesn't work. We had an AI detector turned on unilaterally by the vendor on campus at Vanderbilt, and it started giving scores on assignments. And we really quickly formed a team to go and assess. Are we going to do this? And basically what we asked is like, can you give us the data used to verify and come up with these numbers? They said, no. We said, can you give us details? They said, no, it's proprietary. And then what we did is the next logical step is we took a couple of our theses that were done long before any of this, fed them in, and immediately they got flagged. And so for us, we looked and we said, look, this is going to turn. Get thousands of students sent to the honor council. You know, thousands of assignments would be flagged. And what we were told was, well, we would, you know, the vendor told us, well, we would expect the faculty member to look at the score, but then assess all the characteristics and make a decision. Except that AI detectors tend to be a number. It's like you go into court and it says, 83% guilty. All right, he's guilty. But there's no evidence. Right. Because it's all baked into some model that you can't see. You know, at least plagiarism. You can say, well, here's what they wrote and here's the paragraph, and then you can decide. So I think AI detectors, one, it's like a band aid for not wanting to change. But two, it's just a tremendous disservice to students.

I'm glad. I'm glad we agreed on that. 100. Literally, I've been saying this for year. My exact phrase is snake oil. Yeah, the whole open AI thing, they took theirs down because it showed it was only 26 effective, which is less than flipping a coin. So, yeah, if. If you are listening out there, I'll be more direct. AI content detectors do not work. There is no such thing. We've personally done tests and busted them all and very easily. So I do want to get to one more question here before we start to wrap this up, Jules, because, you know, you are in charge of right now, you know, kind of educating the next generation of leaders there at, at Vanderbilt. But I feel a lot of the lessons that you're learning can be applied to the. The business world, the rest of us. Right. So as someone that is constantly having to learn and innovate and, you know, understand and implement new concepts, what have you learned in the educational setting that maybe is very applicable to the business world and what we should all be focusing on?

Yeah, Well, I would say the first thing is, like, you really need to teach how to use it intentionally so you know that use it to gain perspectives, not use it to give you answers, because you don't want your workforce checking out and becoming, like you said, over reliant or not checking its work. So I think that's one. I think the second thing is not to underestimate the magnitude of this change. I mean, just to go back to what I said at the beginning, I think I didn't believe I would live to see something like this. And, you know, you see so much discussion of what is your return on investment, and that's thinking of it the wrong way. Like it's going to be some incremental 20%, 50%. This is one of those technologies that creates completely new things that we haven't thought of before that create tremendous growth in an area and then make some area totally obsolete or irrelevant. And so I say the ROI is risk of irrelevance. And so you need to determine what is your risk of irrelevance. And if you're not investing in training people, and that is critical, as you're training people, you're supporting experimentation and creativity in this, then your risk of irrelevance is huge. So I think those are sort of the two things. And if we go back to risk of irrelevance, I mean, we wouldn't be talking about our universities going to be here if this wasn't a fundamentally destabilizing, you know, capability. I think that they're the ones. And I, I should probably modify in my answer. The universities that engage and embrace and innovate with this will absolutely be here. Those that do not will be maybe not in existence anymore.

So we've, we've talked about a lot in today's episode Jewel from, you know, the future of, of learning how we can take lessons from the classroom to the boardroom. Examples of what you, you and your peers are doing at Vanderbilt and AI content detectors. We've covered so much, but maybe as we wrap, what's the one most important takeaway that you want our audience to hear and apply today when it comes to the future of generative AI in the classroom?

You know, I think, I think the, the future is augmented intelligence, where you teach somebody how to use it in the right way to augment and amplify their critical thinking and creativity. But it's fundamentally rooted in their critical thinking and creativity, and you want to really, really build on that aspect of it. So a lot of the things that we do right now are based on, like the way technology worked before, the way we assessed before, and we're going to have to reshape that. And the fundamental thing that we're going to have to think about is creativity and critical thinking. It's fundamentally going to be the most important piece. And then all of this technology is going to be how do we harness that amplify it, magnify it. Give them new outlets for creativity.

I think those are such good insights today. So, Jules White, thank you so much for joining the Everyday AI show to help us really make some, some meaning of all this mess of what's happening in the world of AI and education. We really appreciate your time and your insights.

Yeah, thank you so much for having me.

All right. And hey, as a reminder, that was a lot. It was like a course and a half on AI and education. So if you haven't already, please make sure to go to your everydayai.com Sign up for the free daily newsletter. Myself a human, I'm going to go sit down, relisten to this and write some of the most important takeaways and more that we didn't get to. If this was helpful, please consider sharing this with your network or, you know, subscribing and leaving us a review on Spotify or Apple. So thank you for tuning in. We hope to see you back tomorrow and every day for more Everyday AI.

Thanks, y'. All.

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