AI in the classroom: Lessons from the field

Audio (00:00):

Christopher Piech: Isn't that so ironic? We've been working on AI technologies for so long, but in this moment of AI and education, I'm putting my money in the human touch.

Denise Pope (00:11):

Welcome to School's In, your go-to podcast for cutting edge insights in learning. From early education to lifelong development, we dive into trends, innovations, and challenges facing learners of all ages. I'm Denise Pope, senior lecturer at Stanford's Graduate School of Education and co-founder of Challenge Success.

Dan Schwartz (00:34):

And I'm Dan Schwartz. I'm the Dean of the Graduate School of Education and the faculty director of the Stanford Accelerator for Learning.

Denise Pope (00:44):

Together, we bring you expert perspectives and conversations to help you stay curious, inspired, and informed. Hi, Dan.

Dan Schwartz (00:55):

Hi, Denise. Guess what?

Denise Pope (00:57):

What?

Dan Schwartz (00:57):

Today we're going to walk down memory lane. So AI has been around for a couple years now, and it's been a steady theme, both on our show and in discussions about education. So we thought we would check in. But Denise, it's been quite a while now and I'm wondering what kind of relationship have you developed with AI?

Denise Pope (01:16):

What kind of relationship? Monogamous? I stick with one. It's my friend and it knows me and I don't know. What's your relationship?

Dan Schwartz (01:31):

Well, I'm not going to say polygamous. I'll say I'm celibate.

Denise Pope (01:34):

So you don't use it at all?

Dan Schwartz (01:36):

No, I sort of use it like a search engine. I'm kind of lame.

Denise Pope (01:39):

I'll tell you, there's a bunch of professors at the Graduate School of Education here at Stanford who are using it sounds like much more than you.

Dan Schwartz (01:49):

They're using in ways I think that surprise people. So dear listener, Denise and I are going to take you on a deep dive over the very best AI content from the last two years.

Denise Pope (02:01):

That's right. We have pulled together clips and conversations and ideas from across the show to take stock of just how far we've come with AI in the past few years and how far we still have to go. And I really want to start by looking back at an early discussion on the topic of promising ways that teachers can use AI. And we spoke to Professor Dora Demszky. Dora has a way of empowering teachers because teachers don't get a lot of feedback during the day. And so with this tool, it's really cool. It's a way to give teachers feedback, which can kind of reduce sort of that isolation that you might feel like, "No one sees me teaching besides the kids in my classroom."

Dora Demszky (02:41):

Dora Demszky: So we call this M-Powering Teachers, that's the name of the tool that we built. It's really rooted in the idea that we want to empower teachers. We're not trying to tell them what to do. We're just providing them with opportunities to reflect on what they did. So kind of like a Fitbit when you run and it gives you some metrics, it doesn't necessarily tell you like, "Oh, you should burn this many calories a day," or, "You should run this fast." It's so person-dependent and so context-dependent that you are the only person that knows. So we first obviously validate it in different ways, validate it by having teachers, experts look over what our models are predicting and what are they outputting. Do they seem right or is they wrong? But also a lot of the feedback is actually providing them with things that they did, highlighting things and moments in their lesson for them to reflect on and asking them good reflection questions and goal setting questions. So there's less opportunity for risk.

(03:47):

Dan Schwartz: Let's take the Zoom example. It's online. I've got like three or four students that I'm talking to. Afterwards, it'll come back and it'll say, "You said to that student they should try harder. How do you think that student felt about that?" What's an example of some feedback?

(04:05):

Dora Demszky: Yeah. Great question. So you mentioned what you just brought up is something that has to do with growth mindset, which is something we worked on. Try harder. The idea that intelligence is not something you're born or it's not something that's fixed, that you can improve your brain and your skills constantly developing if you are working on it. But just telling them to try harder may not be the best way to do that, but actually recognizing that things are challenging and giving them the tools and strategies to improve their skills. But we are trying to identify these types of practices like growth mindset supportive language. We're building on a student's idea. So we know from decades of literature that when students feel heard, when they feel that their ideas matter and that their teachers are building on it rather than just funneling them to a very specific answer, that really facilitates learning.

(05:04):

It creates that type of a collaborative process that you mentioned at the beginning that it's not just the teacher directing and the students passively receiving things, but instead students are active participants. So we identify practices that are related to that, like building on ideas, mindset supportive talk, asking questions that probe students' thinking. And then we build algorithms. It's not really a chatbot. These are algorithms that can identify moments when you do that and then highlight them for you, for you to revisit, to think about, "What did I do in this moment that led to this great example and could I do this more? Where are there missed opportunities?"

(05:50):

Denise Pope: Dora, can I just say one of the coolest things about this? Teaching is a really lonely profession. You talk to teachers all across the US, it's very rare that they get supervised, that they get any kind of feedback that someone's actually in their classroom for more than like 15 minutes with a sort of high level like, "Hey, good job. Keep it up." And when you tell teachers that you want them to go and look in other people's classrooms and take the time and actually build in time for teachers to watch teachers, it's a little bit awkward. They don't know how to give feedback to their peers. It's not part of that culture. So you've kind of taken the ouch out a little bit and you've also allowed more camaraderie to happen.

Dan Schwartz (06:42):

Denise, that was a great follow-up commentary on what Dora had to say. So here's what I want Professor Demszky to do next. I want her to take this application and make it for podcasters. It could say things like, "Dan, maybe you shouldn't talk so much. Let the guests speak."

Denise Pope (07:02):

Let's definitely try to do that in one of our next shows. I love that idea that you could get feedback from an AI tool to make our podcast better. But honestly, I do think that the way Dora's using this, you get the feedback you want. I think there's so many applications of it.

Dan Schwartz (07:18):

I agree. It sort of opens your imagination to possibilities.

Denise Pope (07:22):

It does.

Dan Schwartz (07:23):

So I want to take the example of English because people think about AI and its relationship to writing and reading.

Denise Pope (07:30):

Yes. In fact, a lot of English teachers I know are really, really worried about AI. They want to go back to blue books.

Dan Schwartz (07:37):

Do you have a position on this?

Denise Pope (07:38):

I think that we can't isolate ourselves from AI completely. We're going to have to figure out a way to use it. And what I love is we actually have someone at Stanford, Professor Hari Subramonyam, who is using AI not to get rid of writing, but he's using it to structure this really cool writing process for students. So in a way, he sort of takes the ouch out of writing. Let's listen.

Audio (08:05):

Hari Subramonyam: We have what we call writer's friends that live in these pages that you can call up anytime you need help. So we have an Idea Ivy. So if you're stuck in the ideation phase, you can call Idea Ivy and sort of chat with Idea Ivy about what you're trying to ideate on. If you have written an initial draft, you can call another friend called Structure Sam, who's going to help you explore different structures for this idea. Or if you have a vague sort of argument you have made and you want more details about it, you can invoke Detail Danny, who's going to then help you flesh it out. So we have these writer's friends who live on individual pages. Some friends help you with writing, what we call scripting. Some friends help you with thinking through variations of what you have written, which we call shifting, like exploring different rhetorical strategies.

(08:55):

Dan Schwartz: Wow.

(08:56):

Denise Pope: This is an English teacher's dream. Instead of just having the kids get better at prompting ChatGPT, you're actually building in some of the things that I, as a high school English teacher, would've wanted my students to really think about in a kind of fun way by naming them and not just like, "Here, AI, just write my whole thing." I love that.

(09:19):

Dan Schwartz: So I can't resist. Is there a Critical Thinking Karen?

(09:22):

Hari Subramonyam: There can be a Critical Thinking Karen. So these friends are configurable by the teacher. So just a little bit of background, before we built this tool, we interviewed over 20 English and history teachers and we co-designed a set of prompts, looked at English grading rubric. And we sort of worked with teachers to understand where would they want students to get help from AI, where are they struggling, and what are the things they don't want the AI to do for students? So all of this is sort of built into the tool, which is why, as you said, Denise, it is the teacher's dream.

(09:57):

Dan Schwartz: A lot of people say that ChatGPT, which is very capable of writing prose and arguments, is going to displace people's opportunity to learn how to do critical thinking.

(10:11):

Denise Pope: I'm very interested in what you think critical thinking means. What's the definition to you?

(10:18):

Dan Schwartz: I actually have a definition of it, but I'm not sure-

(10:21):

Denise Pope: I want to hear it.

(10:21):

Dan Schwartz: I'm not sure anybody else subscribes to this definition, but it's the processes by which you decide what to believe. So it could include what kind of evidence do you look for? Do you look for consistency? Do you even choose to seek evidence? Things like that. So do you check sources? So it's a little more complicated.

(10:44):

Denise Pope: Yeah. Well, when I teach qualitative research, one of my questions is how do you know when a claim is true? This seems kind of similar, right?

(10:52):

Dan Schwartz: Yeah.

(10:53):

Denise Pope: Like what would you do to check that? How would you even know how to think about it?

(10:57):

Dan Schwartz: Yeah, yeah. Yeah. No, I think this would be a very specific brand of critical thinking.

(11:01):

Denise Pope: Yeah. So Hari, what do you think?

(11:03):

Hari Subramonyam: Writing is a way for people to engage in critical thinking. It's only when you're writing that you're trying to identify what is the context and what kind of ideas you have, what kind of connections do you need to make between these ideas? Are you having a logical chain of reasoning from some idea to evidence to insight? Are there gaps in your ideas? What are the evidence? With tools like ChatGPT, students can give very little input and get lots and lots of text as output. So there is this imbalance between how much input you give and what kind of output you get. So if I'm trying to write an essay, I can just go to ChatGPT and say like, "Okay, help me write an essay about healthy eating and nutrition." And it's going to give me a three or four paragraph long essay. And I think that is when the student or the learner is not actively participating in shaping the essay, a lot of the creative and critical decisions are made by the AI.

(12:02):

And this is sort of what is problematic. This is partly why we see lots and lots of essays that sort of look and sound the same because people aren't bringing in their authentic ideas and insights into shaping the essay. Now in theory, you could argue that students can break down their arguments and instead of asking a ChatGPT to give the essay, they can do this step by step by asking you to list out ideas, brainstorm with it, and all of that. But this kind of engagement is hard to do in practice because these tools aren't designed to support that. And as a human computer interaction researcher, what would we find is that the human AI interface is missing the kind of inferences necessary for exploratory dialogic, critical thinking and writing.

Dan Schwartz (12:53):

Okay. So I really like that clip where Hari sort of helps kids go through the stages of writing rather than having it all done for them.

Denise Pope (13:01):

I know, right? Because it's so tempting to push the easy button and just have it spit out an essay for you.

Dan Schwartz (13:07):

I know. A lot of people think writing is a way to develop critical thinking. But Denise, I have a scary proposition for you.

Denise Pope (13:15):

Uh-oh.

Dan Schwartz (13:16):

I think AI does it so well that teachers are going to spend less time teaching writing. It may be dead is the major subject.

Denise Pope (13:23):

Okay. First of all, as an English teacher, this is killing me. I don't think writing is dead, but I will give this to you. I do think that writing is going to be more hybridized. I think it's going to be a combination of AI, maybe doing a draft and then a human. And I'm just not sure how I feel about all this as someone who loves writing and teaches writing.

Dan Schwartz (13:44):

Okay. So the hybridization where the sharing of the human and the AI is sort of a vision that a lot of people have. And I think we have a great clip from our chat with senior lecturer, Karin Forssell. This can be perfect for this. She gets at the question, how do we get kids to engage and still make it a hybrid experience where they're sharing the responsibilities?

Audio (14:11):

Karin Forssell: One of my favorite examples of somebody who has done this is one of our colleagues with a three-year-old daughter and they wrote a story together. And the child can tell a story. And by the way, through natural language processing, she can just talk it and the computer will then transcribe it and then generate pictures to go with it. And then you print it out on a color printer and you've got a storybook authored by a three-year-old. I mean, that's really cool.

(14:38):

Denise Pope: Okay. You got me there because I know not everything has to have outcomes, but the outcome there is potentially getting a kid very excited about literacy and about reading and about creating and understanding the connection between pictures and words. And also something you said, Karin, that really struck me is this idea of learning how to collaborate and create and think through problems. Those are incredibly important skills. So it's not just we're going in and we're making some sticker books. We're actually doing things to teach skills that kids need in the future.

(15:14):

Dan Schwartz: So the great thing about the GenAI is that people really like to make stuff. Now the challenge is, can we have it so they get to make stuff that they really like to make and it forwards their learning as well on things that don't necessarily always involve using GenAI? So a simple example is I learned to write by co-authoring with someone who's a better writer than I was. I would write at night and then he would take it and just completely rewrite it in the morning because he learned that editing's too slow. And then I would do again at night and I'd see all the moves he made. So I can kind of do this with a computer now. And the hypothesis is that I'm going to be a better writer when I see how good it is at saying, "There are three points that we're covering."

(15:59):

You can imagine someone whose English isn't their first language and they get a chance to work with these tools and play with language. So the trick is how can you make this sort of creative generation cash out in domain knowledge and skills?

Denise Pope (16:17):

I remember when we taped that with Karin, that I didn't really understand that last line when we were taping it. And I'm not sure I understand it now. Can you explain what you mean by cashing out in domain knowledge and skills?

Dan Schwartz (16:30):

Oh, Denise, how many months have you been walking around wondering what does cashing out domain and skills mean? I'm so glad I can finally clear it up for you. So domain knowledge is generally things like discipline specific. Like I have knowledge about science. Domain knowledge would be sort of knowing a linear equation. So there are kind of specific bodies of knowledge tied with areas of inquiry. And so we once did a study at a museum that had a makerspace and the kid's task was to create these sort of helicopter type things that would float in a wind tunnel. And their goal was to get it to float it and stay right in the middle. And afterwards, we gave them a choice of videos to watch. They could watch a video that explained the underlying science or they could watch a video that showed other techniques for making their thing float. And they all chose to see the techniques and to make their thing float.

Denise Pope (17:25):

Yeah. So would I.

Dan Schwartz (17:27):

Right, right. So this is the challenge. You have these really compelling experiences and how can you get the science in there? It's hard to get them to learn it during the project because the project's so exciting.

Denise Pope (17:38):

Okay. That totally makes sense.

Dan Schwartz (17:40):

Okay. Finally.

Denise Pope (17:41):

Thank you. That totally makes sense to me. Okay. So there's certain things when we're using AI, we want to make sure people keep doing, but I want to bring up, there's some other really important key points that we also want people to learn. And that is that AI is not always telling the truth, that AI can be biased. And in our next clip, Professor Victor Lee tells us about some of these really serious issues of bias in AI.

Audio (18:08):

Victor Lee: It is not the same as a human. It can do things that are pretty human-like, but it's still not human. And that's really important because humans make really life-important critical judgments and we need to make sure humans are still doing that. It also means that it doesn't have sort of the same way of thinking about telling the truth or being accurate in the way that humans are sort of socially accountable for. So that's something else is that while it may sort of sound human or realistic, the information we don't know is reliable because it's sort of generating it on the fly and it doesn't have the capacity like we do to kind of check, was that right? Did I say something for real? And it makes stuff up. I mean, that's what they call the hallucinations.

(18:59):

Denise Pope: And I know there's a pretty cool lesson on there about AI and bias or issues around bias with the technology. Can you just say a little bit about that? Because I think that's part of the hoopla that parents are nervous about and that teachers are nervous about as we're hearing like, "Well, could be promoting some unfair biases."

(19:19):

Victor Lee: For sure. Yeah. With AI and any sort of new technology, it lets things happen faster. And for bias and bias to sort of be put out there faster and in larger amounts is kind of a scary thought. But the bias that we do see is because AI is trained on data and the data that we have can be really incomplete. So if the data are only trained on people who sound like Dan Schwartz and then it hears Denise Pope speaking, it may not be able to understand what Denise Pope is saying just because it only is used to what Dan Schwartz can say. Well, think about that with all the images that are being used on the internet or the ways that people write or talk. And so those are some of the concerns. And because it can make things go fast and it can get around so quickly, it kind of raises this risk of bias amplifying at a rate that we just would not be comfortable with.

Denise Pope (20:16):

Really important lesson that Victor's bringing up about bias and hallucinations. And I feel like every time we feel like we're getting closer to how to use AI and what we like about AI and how best to think about it, three more warnings pop up.

Dan Schwartz (20:33):

There's a lot of efforts to control the bias and the hallucinations, but they're still going to sneak in. And so whether it's the bias or a hallucination or for us just trying to figure out what to trust, it all points to a collection of skills that are becoming more important than ever. And this is digital literacy. And so we're bringing in a clip from our resident super expert on this, Professor Sam Wineburg.

Audio (20:58):

Sam Wineburg: So what is digital literacy? On the most basic level, it's the ability to go online and discern what is true from what is sham. So a couple of examples. In Springfield, Ohio, were Haitians grabbing their neighbor's cats and stringing them up and barbecuing them? This is a claim that ultimately made its way to 67 million Americans. And so the ability to look at something like that and decide, "No, I think I better check that out." Now let me say in the same breath, more recently, what's gone viral is the claim that Elon Musk turned the election using Starlink for Trump and that actually the winner was Harris. So again, this is the kind of thing that can be established if you know what to do, but if you don't know what to do, you become part of spreading digital pollution. And so that's at a basic level, given how much time and how dependent we are on our digital devices, it's the ability to have confidence in information about whether to believe it or whether to kind of hold off and say, "Probably not."

(22:17):

Denise Pope: So last time you were on the pod, you did a little test with Dan and I, and we failed. And it was how do you know if you're looking at something, what's the first thing you should do to sort of make sure that it's true or make sure that the facts are correct? And the answer, and I took this with me and I do it, is to look at Wikipedia. Is that still the same answer? Is that one of the things that you would say to do or are we beyond that now?

(22:44):

Sam Wineburg: That's not quite, if you check the podcast, that's not quite the response that I had.

(22:48):

Denise Pope: See? This is what I'm saying. We failed. We failed. We're still failing.

(22:49):

Dan Schwartz: It's funny, all I remember is that I got the wrong answer.

(22:54):

Denise Pope: You didn't even know the... Okay, okay.

(22:54):

Dan Schwartz: I have no idea what he said.

(22:55):

Denise Pope: That's so funny.

(22:58):

Dan Schwartz: I have no idea what he said was the right answer.

(22:58):

Sam Wineburg: Let me provide a small, gentle correction.

(23:01):

Denise Pope: Thank you. Thank you.

(23:02):

Sam Wineburg: I asked you a question if you come across a website, and let me give an example and you can folks take this one at home. Theinternationallifescienceinstitute.org. It claims to provide vetted health information that has been approved by a scientific board. You look at this website, you see that there are scientific reports. You see that there's a peer review journal. And I said, "What do you do when you come to a website like this?" And you started to go into critical thinking. And you said, "I will look at the about page. I will look at the scientific advisors." And I said, "Wrong answer."

(23:40):

Denise Pope: Yes, I recall.

(23:41):

Sam Wineburg: Unless you have all of the time in the day. You have a limited amount of attention. And so what you should do is you should engage in critically ignoring rather than critical thinking. If you don't know what something is, what you should do is to use this incredible device that we have, an internet in which sources are electronically linked, and leave that and use the power of the internet to gain some context of what that organization is. And within a few seconds, if you leave it and open up multiple tabs, a process that we've observed with professional fact-checkers, which we call lateral reading, you can learn through context that this actually is an organization that is funded by big soda, by big candy, by the agribusiness. And so Wikipedia is one among several sites.

(24:32):

Particularly you can go to Wikipedia. Now again, what does Wikipedia provide? If it's a well-trafficked site, there are references at the bottom. And if you recognize something that there's to the Wall Street Journal or to AP or to the New York Times, whatever news vetted organization that you can kind of corroborate, that's your first move rather than devoting all of this time on a site for which you are not an expert about the topic.

Denise Pope (25:05):

What Sam was talking about I think is so critically important in today's day and age. I really hope that AI doesn't continue to hurt our ability. We need to critically consider everything it tells us. And there's this little metaphor that people have been talking to me about, mostly educators, about reminding us to be the human in the loop. Some people call it the AI cookie. So first you do some human thinking, then you bring AI in to help you out. And then really important, the human comes back in to check it all over.

Dan Schwartz (25:37):

So I'm not sure that's a cookie. It might be an ice cream sandwich, but I really like the expression, the critical thinking cookie. It's so appetizing.

Denise Pope (25:47):

It's a critical thinking cookie. Be the human in the loop. Remember, AI is just a part of the pathway that you're taking. And this reminds me because we touched on that human element when we were doing our live taping in Los Angeles when we did an AI show. And in LA, Stanford computer scientist Chris Piech told us how he keeps humans in the loop.

Audio (26:09):

Christopher Piech: Education hasn't always worked for everyone. There's a lot of people for whom the learning system wasn't the dream that we imagine. You have young children and they have this joy of learning. They have this thrill when they can do things that they couldn't do before. Even if it's something difficult like learning to read, write, or even walk. And somehow this is a great opportunity to get closer to that dream. We have a moment where maybe we can create that sort of education that would work for more people and would allow people to hit higher heights. I want us to choose to seize this moment, not be so fearful of it, but let's create that future that will support the Stanford students and the learners abroad.

(26:44):

I actually have talked to a lot of people that in this moment of AI, when we all have these new affordances, the thing that will really distinguish great teaching experiences is the human touch when some students are feeling lonely and they're looking for that mentorship. I really do think the special thing about maybe a Stanford education is not always just the problems you're solving, but also the people you're meeting along the way and those connections. So isn't that so ironic? We've been working on AI technologies for so long, but in this moment of AI and education, I'm putting my money in the human touch.

Denise Pope (27:16):

So Dan, that is such an important point that Chris is making about keeping people engaged and excited and the equity piece that this could work for everybody. And in our next clip from the same live episode is Professor Judy Fan. And Judy shares Chris's optimistic vision for the potential of AI and she's really excited about the way it can boost engagement and learning. Let's listen to Judy.

Audio (27:40):

Judith Ellen Fan: So one of the ongoing projects in my lab is an effort to empower fourth grade students to build math games that are really immersive, that also genuinely help them learn math and that are relevant to their interests. And I'm super excited to see where that's going.

(27:58):

Denise Pope: Can you give us an example of immersive math so people know what that means?

(28:02):

Judith Ellen Fan: Yeah, absolutely. So there are a lot of educational games in the app store right now that are essentially worksheets with some animations and sound effects thrown in. And those are pretty shallow. The kind of gamification that you're looking at there is really quite shallow, even though they might be engaging in the sense that a kid might spend a lot of time playing that game. What we're really trying to do is help lower the barrier of entry for kids who may not yet know how to code, but they can imagine a world where they're say learning how to bake or they're learning about fractions. It's an actual world that they get to build an author that is where the graphics and characters are ones who they come up with. And the mathematical concepts are actually embedded in the storyline. And the stakes of the story and the interactive story that's told in the game actually engages and invites discovery of those mathematical concepts.

Dan Schwartz (29:21):

So Denise, that is an example of integrating domain knowledge and skills into projects.

Denise Pope (29:28):

Aha, I get it. I get it, Dan. Thank you. It makes a lot of sense.

Dan Schwartz (29:31):

So now the conversation's going to transition to responsible AI, another big topic. And we're going to have another opportunity here from senior lecturer, Karin Forssell. She's going to talk about the AI Tinkery. This is an AI makerspace for educators where they get to come in and see different kinds of applications, different things they can do.

Denise Pope (29:52):

So let's hear from Karin talking about how to teach responsible AI use.

Audio (29:57):

Denise Pope: How do you make sure you're teaching them skills for good and not skills for evil? Because I'm seeing a lot of the potential for deep fakes, right? And for people who don't know what that is, creating pictures of a girl in your class without her top on. So are you building in any safeguards at the AI Tinkery?

(30:19):

Karin Forssell: Guardrails?

(30:20):

Denise Pope: Yeah.

(30:20):

Karin Forssell: I don't know exactly where we will wind up with this because we're sort of evolving as it becomes real. But I do think that educators are always going to be very interested in discussing the ethics. Obviously there are lots of interest in the bias that comes from the training set, being biased to start with. What kinds of issues might there be if students put in a prompt and get something that's totally inappropriate, disruptive, but also misleading. This is my prediction. Over time, we're very early days right now. This gold rush time. We're going to see more competent, more evolved tools over the next few years. And I think one of the things that we're going to see is sort of more safe spaces for education that are not trained on everything on the internet, but on a quality over quantity for what kinds of answers the AI can give.

(31:11):

So we're going to see what kinds of limits it provides, but I think that in schools, we're likely going to have very targeted use of AI tools that help you to do particular things really well, like compare and evaluate. I sort of suspect that our curriculum, it's like when we got the calculators. Remember when all the debates we had about letting people use calculators in school or on tests? Oh, my goodness. With AI, I think we're going to find that we use these tools as helpers to get students to the place where they can recognize good work and they can go further, faster and level up with what kinds of writing they're producing. That's my hope.

Dan Schwartz (31:58):

Okay, Denise, these are amazing clips. These are interesting and challenging times. It's only been a short while since the dawn of AI, or at least generative AI. And we're already diving into topics as diverse as writing, critical thinking, creating, verifying, bias. That's why it is still all AI all the time here at Stanford. So let's end with Judy Fan, who helps us zoom out on the future of AI and education.

Audio (32:25):

Judith Ellen Fan: We have to decide what we value, what our values are, and we may not agree all the time on what we value. And that's the hard part, actually. The engineering, the design, that's something that we should engage in such that the learning environments that we build express those values and give dignity and agency to our lives and that we can actually build a world that we want our kids to grow up in. And I really think it really comes back to what we value and the technology follows that.

Denise Pope (33:06):

I love that clip from Judy. I love that we're ending with, it comes back to values. It comes back to what we value and the technology follows that. So Dan, thanks for joining me on this walk down memory lane. Super fun. And thank all of you for listening to this episode of School's In. Be sure to subscribe to the show on Apple Podcasts, Spotify, or wherever you tune in. I'm Denise Pope.

Dan Schwartz (33:29):

I may be an AI hallucination of Dan Schwartz.

Denise Pope (33:34):

Oh, my gosh. It's been a bot all along.

Dan Schwartz (33:36):

All along.