The AI-list: A Clips Episode on AI Across the Classroom

Welcome to Educast three thousand. It's the most transformative time in the history of education.

So join us as we break down the fourth wall and reflect on what's happening. The good, the bad, and even the chaotic. Here's your hosts, Melissa Lobel and Ryan Lufkin.

Hey, everybody. Welcome back to Educast three thousand. I'm your cohost, Ryan and today, we're gonna do something a little bit different. We've been having these incredible conversations about AI in education, and honestly, the same big questions keep coming up no matter who's in the seat across from Melissa and I.

What should we actually be teaching? How do we know students are really learning? And what does it mean to be a good citizen in an AI powered world? So for this episode, we've pulled some of the sharpest moments from some recent episodes where we've spoken to all kinds of folks, including a computer scientist, a historian, a learning scientist, and a university chancellor, and we've put them all in one place.

Different perspectives, different roles, and sometimes surprisingly similar answers. So without further ado, let's get into it.

I mean, you're practicing what you teach in so many ways. It's the fundamentals of transformational learning is is applying directly to transformational leadership, which is so powerful.

I'm curious and and we can't not ask this question. What role is AI playing for you in all of this? Or how are you thinking about AI as you are continuing to ensure your students are having these incredible experiences that you're committed to?

AI is the future of transformational learning. It is a major innovation that is already changing and shaping the future. And in higher education, we have to get on board and accept that AI is our friend.

It is our compadre and not our enemy. So when I think about what's next for transformational learning at Kane and in our Kane Global portfolio, we are focusing very intentionally on, you know, some of these new trends like artificial intelligence, providing micro credentials, really maximizing the use of immersive technology to assist and develop learners and to again, help improve outcomes. So we use the word trends in higher ed, but these are really much more because they become tools for how we reimagine the learning journey for students. And, you know, at the end of the day, it has to be utilized in service of human outcomes, which is why we see AI as our friend.

So at Kane Global, we're exploring how AI can personalize the learning experiences of our students while strengthening their sense of belonging. We're expanding our digital and physical access points across continents, again, in ways that allow us to leverage AI and these immersive technologies to support students, to support development. And the next frontier is really going to be creating what I like to call these borderless ecosystems where transformation isn't just possible. It becomes an expectation.

Yeah. We usually, at this point, ask, like, get out your crystal ball. What do you see in the future? You guys have already been, like, looking deeply into the crystal ball. It's pretty clear.

It it it's it's doctor Ruppolet skating to where the puck is gonna be, or I should say Wayne Gretzky. But nonetheless, that's a part of how we continue to innovate. One of the taglines that we have in transformational learning is that we have to relentlessly innovate. Yeah. So relentless innovation is the framework that allows us, to really continue to be ahead of the curve.

And relentless innovation is not necessarily a term highly associated with higher education.

Absolutely correct.

But, so so for institutions who might be looking to kind of start embracing that innovation, start, you know, going on their own, transformational learning journey, what's one piece of advice around even just getting started?

Every initiative for me, and this is who I am as a researcher, as an academic, as a scholar, and as a person is you have to start by asking the why, what questions. Ultimately, it's a human developmental piece that we should be asking periodically across the life course anyway, the who am I, what am I question. Well, we have to do that organizationally as well. So my advice to institutions that recognize the importance of really embracing this emerging and evolving population in higher education is to really ask the first question, what do our learners need to know to transform?

And not just what we want to deliver to them, but how do we build systems and structures and strategies that recognize the lived experiences of our students, that prioritize reflection and that foster connection? You can't allow the complexity, particularly of working with new populations to paralyze you.

Start small, but also really take advantage of the lessons learned in terms of what that reflection and self appraisal represents to embed the practice of prior learning recognition into what you're doing. So in New Jersey, for example, there are over a million people who have some college but no degree. Yeah. It's a huge opportunity to, again, really allow those people to have the platform access to go back and finish what they started.

Absolutely, yeah.

But we have to build systems and structures and strategies that are aligned to that. And the traditional higher education model simply is not.

Just the high school graduate funnel doesn't map to that at all.

That's right. And ultimately what we position is the orientation that how we have structured the programs, what that means in terms of the content, the delivery modalities, positions us to really be clear that transformation is not just about pedagogy. It's really about how do we fulfill the promise Yeah. To these students and how do we make sure that we're creating systems that support that and that ultimately are not barriers to their success.

Yeah. The only downside to this being an audio podcast is you can't see Melissa and I nodding along aggressively.

And you smiled.

I feel that. I see it.

So I wanna share that with the listeners that Well, I love it.

All very connected around these themes and issues. So that's why this has been such a delightful conversation for me.

Well, I think one of the things that's so interesting is be with your background, you know, other than the fact that you're probably you spent more time across different regions of the globe than anyone else in education that I know of. It's fascinating. But but you also contributed to the the UNESCO development of the AI competency frameworks for teachers and students, which we've shared previously on our show here. That's good. But I I love that that you know, tell us a little bit about that effort, I think, because I think it's it's incredible that we were able to have a group of educators and really intelligent people from across the globe come together to provide some guidance that, you know, now we're seeing individual states, individual schools use as their guidance.

Yeah.

I I really like this piece of work.

It's one of my favorite things that I've been involved in, and I really have to thank UNESCO and in particular Feng Chen Miao for the opportunity.

Essentially, so there's a a big event at UNESCO every September that was mobile learning week and now has become digital learning week. And what started to happen, I think even as early as twenty eighteen, was this this expanding conversation around, you know, AI and AI skills and AI literacy and what AI fluency looks like and and essentially quite a lot of conversations conversations lamenting the fact that this was mainly being defined by industry, which is not always a positive thing. I'm not saying industry is all bad, and they do know quite a lot about skills. But for example, I did just another thought exercise looking at an eighth grade curriculum from one of the big tech companies that was being rolled out in India and the MIT AI ethics curriculum for middle schoolers.

And so I did a side by side comparison of how they approached ethical issues, like what ethical issues they approached and how, their approaches to project based learning, and then overall just kind of their epistemological slant. And what I found was essentially that, you know, it's not surprising, but the MIT curriculum was really concerned with issues of privacy, ownership, data, looking at the environmental impact of AI, making good, conscientious decisions, looking at the hidden agenda behind, for example, YouTube's algorithm, what is it actually trying to get out of you versus what it says.

And the company's approach to ethics was to look at intellectual property and how AI improved to disabled populations like blind, deaf, you know, these types of text to speech, speech to text. And that's it. Not a word about transparency, not a word about explainability, nothing about privacy or bias or data.

Again, this was just looking at one grade, so it's possible these were covered elsewhere.

That was really fascinating.

Project based learning was also very much a co creating process in the MIT curriculum and in the other curriculum. It was very much use our product X to do Y.

Yeah.

And that was a project. So just a very different view from kind of the industry world and the education space about what education actually is and what learning processes should look like, what project based learning is. And so what I love about the UNESCO project is that, think tune in particular led the charge to say, well, let's let's come up with an alternative. Yeah.

If we don't this, let's do something about it.

I love it.

We could do something about it. And so we worked almost two years on this framework, tapping into experts from around the globe. We did talk to industry quite a lot. I worked also closely with Natalie Lau from the MIT App Inventor Foundation.

She gave quite a lot of the technical expertise that we needed to complete the framework, and I I think it's really well balanced. You know, it starts with this human centered perspective.

It's very much about what do we need AI to do for humanity and reflecting on that question.

Started from that perspective. That was actually really meaningful. Yeah.

Yeah. And then on the ethics side, I mean, there's a whole there's a whole you can read the frameworks quite long, but the main core principle there in the ethics side is proportionality.

Are we weighing the risks and the benefits and then making a good decision based on that? Yeah.

You know, and I call people out on this sometimes. Recently on LinkedIn, one of my colleagues posted this, Oh, I put my itinerary into ChatGPT and asked it when to take my sleeping pill. And I wrote back like, Come on, man.

Absolutely.

That is not a tough calculation.

No.

That is You can do that.

I know you. You have a PhD.

So so it's very much thinking thinking through that like are you using AI to spit out twister moves like don't do that. Yes, it's for things that you need it for.

Yes, that's all of them.

And then it goes into also what we can expect in the K to twelve space from the technical skills, right? So it's very much there's a technical skills piece and then there's also an applied technical skills piece. And that's really important, especially for female learners. What we know about women in tech is that they're not as fascinated by the technology. They're much more fascinated about what technology can do for social issues. So incorporating both of those pieces is a way to also try to balance AI gender divide.

So interesting continuing on these frameworks. So, UNESCO led the work and have very distinct, but I think very they're meaningful together, but frameworks for students separate from frameworks for teachers. And as we've watched Ryan and I have watched that's not always been the case like different organizations are publishing their frameworks. We watch higher education institutions do that.

And a lot of times they're merging the two. Why two separate? What went into that decision making? And I see the value, where how has the community reacted to having those distinct sets of frameworks, but at the same time, again, they're very complementary?

Yeah. So I would really recommend talking to Mutlu Kukurova if you haven't already. He he led the teacher competency work and also did an amazing job there.

And one of the first conversations that we had actually was do we combine them or not? So this is a great question. We definitely spent a couple of months tackling this question.

And interestingly, you know, we went off. He did his framework. We did ours. We came back together, and we found that actually we covered some of the same things. Actually, first two strands, the human centered piece and the ethics piece, in both frameworks. There is a common grounding there.

And there is also an argument that teachers are students and they should all, you know, True. I mean, the the student framework lays out the AI skills we really think everybody should have. Yeah. So there's there's a reason for teachers to also invest in that space.

But when we think about the specificity of what teachers do with AI, we're expected to do with AI, and you can see this kind of amazing push towards a huge amount of responsibility being placed on teachers. Don't know if you've been paying attention to these policy conversations, but they're essentially calling on teachers to hold big tech to account, which I don't think they should do. But we do want to at least give them the skills to be able to evaluate the technologies that they're using meaningfully. So the teacher framework covers things like assessment, for example, and pedagogy.

So how can you expect or should you think about AI as you think about integrating it into your classroom practice and your pedagogy? So it's less about concretely what AI skills teachers need to give to students, and it's more about the teacher facing question of how should you use AI in the teaching and learning practice. We don't expect students to master that, and we may not expect teachers to master all of the competencies in the student framework. When I workshop this with governments, one of the points that I make is that if you look at the competency framework, I have this little slide that has a circle, and it has all the competencies on it.

Which subject covers all of it? And the answer is none. Know? So you really have to think of this as a cross curricular type of exercise unless you want to really bring in almost a new type of specialist because you couldn't just bring someone from industry either.

You'd have to give them all the ethical skills and you couldn't just bring an AI ethicist. You'd have to give them all the technical skills, right? So even creating a new position that covered all of the competencies would be quite difficult at this point.

So you have to think about, you know, which teachers are best suited for the human centered conversations, debates, and which ones are more suited for the more technical tasks.

So I recently chatted with a dean of a computer science department, clearly a STEM department. And she was telling me how, technology in particular is reshaping their discipline. In fact, she said she's having an existential crisis actually because of that. And she talked about how STEM and computer science has done such a good job for so long marketing itself at least over the last ten, fifteen years as an essential discipline and they've attracted all these students.

And now here comes along technology, particularly AI in her case, but technology in general that is completely reshaping what she should actually teach in her discipline. And that's creating this like consternation around what does it mean to have this degree and what does it mean to, you know, go out into workplace? Well, I'm thinking now on the social sciences side or the liberal arts even, technology is absolutely reshaping it. How do you see tools like AI in this case or big data, digital collaboration, online access to learning, you name it, intersecting with liberal arts education?

And do you see it actually helping fields like, you know, my fields, history and political science or sociology or even broader liberal arts?

That's a great question.

I mean, talking about liberal arts generally, my fluency is best with history. So to start there, you know, history is a discipline that studies the past. So part of the truth of the matter is is that they are never going to be the earliest adopters of any technology team.

I hadn't thought about that, but, yeah, that's kind of I could see what I know.

But look. But but here's what happened. In March of twenty twenty, every history instructor, and for that matter, every humanities and social science instructor, and for that matter, every instructor, period, was forced to rethink how they can provide excellent in teaching and how students can receive an excellent learning experience without being face to face.

And they did it. And they did it through technology and they did it through collaborative learning platforms like yours.

And it was a moment that there was no going back from. It was the mo- a moment where every instructor, even a resistant instructor, was forced to think to themselves, heck, if I just apply a little bit of ingenuity to this, technology can actually make my learning experience better.

And I think that has, in important ways, opened up the doors to, even in resistant disciplines, thinking through the practical benefit of technology. Talking about AI specifically, I always think the analogy I always come up with is how instructors sort of faced Wikipedia fifteen years ago or so, which is when I started to teach.

And at that time, the starting point, at least among a lot of history instructors, was this kind of posture of wariness that Wikipedia was just this this place that students could visit to get misinformation or to encourage them to just cheat in new ways.

And over time, that changed. Over time, you would see classes and departments host things like hackathons where students would go into, say, a history oriented wiki page and edit it and correct those inaccuracies and improve it and begin to do some of the actual work in of of of of history. And you saw other instructors using Wikipedia as a as a way to have really thoughtful conversations about about about sources and evaluating sources and how you can assess knowledge and how you can decide, you know, what constitutes truth.

And that's all using a technology that was scary in a in a really beneficial way. And with AI, I think it's probably going to know go something like that. And I don't know exactly what the outcome is, but I saw this article, which, it was in The New Yorker. It was actually it was titled something like, can the humanities survive artificial intelligence? And it was written by did you have you seen it, Melissa?

I saw it. Yeah. It's really interesting article.

Was a Princeton professor. It was it was and it kind of talked through his process of of how his students kind of thought about AI over time. And what really stuck with me was this point he made at the end, which was that, look, AI may make it easier for students to get the answer quickly, but we're always gonna need humans to ask the questions.

Yeah. Yep.

And that was like, that was like a real, like, light bulb moment for me. And it strikes me that it is uniquely the job of humanities and social sciences to teach students the skill of learning to ask really good questions. Yeah. And so if that is the case, then, of course, there's going to be a practical use for AI, as applied to the humanities and social sciences over time.

Well, the other thing I I think is so interesting is there's also just the the ability for AI to help create engaging content. Right? I think there's this gap in content. There's my favorite account on TikTok is a woman called the culture muse, and she's actually a she's an educator that works for the American Council for International Studies.

And she does little snippets on obscure historical sites in Rome and Greece. And it's these little microlearning pieces that I am obsessed with. I think it is amazing. And so even, like, collecting those different technologies and and introducing those those elements into courses that, you know, it doesn't just have to be a a tome, you know, a tome of reading.

We can actually create make a more interactive path.

Yeah. I mean, that's the thing. And I'm sure you think about this as often as I do, but digital learning should be able to improve the quality of the learning experience. It's not just offering it in a different way. It's not just because books are so expensive. It's because technology allows it to be, like, measurably better if we let it.

The challenge is even at I see this in higher ed ed ed ed, but it's more widespread in k twelve, but this idea that we can't just move beyond the idea that, AI is a a cheating tool. Right? A lot of educators are very hung up on the fact that it is it can be used for cheating. And because it can be used for cheating, they they don't get creative in its use cases, or or they don't start thinking about all available use cases. And and so I I love that you're modeling that behavior because I think they they need to see what it's capable of so they can understand that it's more than just a cheating tool.

Yeah. We we know and Emma Moss from Canyons and Jonathan Stewart over there, they've done some amazing work, they're leading out, I'd say, some national conversations around this. But the they they've come up with a really interesting chart that you guys can link to if you'd like to that's around AI adoption. It's like a ten layered thing.

But one of the earliest conversations is getting stuck on plagiarism.

I always think about it like this, is that when we work with teachers, there's a continuum here when around AI. And the first first layer is productivity. They they start to see AI. Once they get past the fear based conversations around plagiarism, around and let's face it. With plagiarism, it's the the the the the the those conversations were happening long before AI.

Exactly. Yeah.

I remember being a teacher in twenty ten, and I had students buy papers off paper mills.

Yep. There's a reason Chegg's stock has dropped aggressively since AI came out.

And so we have to remember that this is an ongoing conversation, but if we are able to harness the conversations around AI in interesting ways in our classrooms, then that's actually gonna cut down the amount of plagiarism. It's just a matter of understanding the underlying technology and really working it through. And so with teachers, one of the things that I point to a lot is that when we start looking at AI and you get past those fear based conversations, you move into productivity. And productivity is great. Like, writing a lesson plan to fly out faster, getting through the administrivia stuff, like, making sure that your work goes flat down.

That's a good one. I've got a bar in that. Trivia.

I gotta that one goes to Karl ******, one of my buddies. He he he taught me that one.

And so when we when we think about that that productivity conversation, if we get couched in that, though, if that's the only way that people see AI Generative AI, then that's a problem. Because then it only becomes a tool that helps me basically speed up my workload. Yeah. AI, though, when it really gets down to it, is a tool that really thrives on creativity.

It it can ignite people. It can curiosity is a big part of how it functions. And so if we're able to actually dig into how AI functions as a creativity enabler, you can really do some interesting things. And so when we push on one of the my favorite conversations I have with teachers often is I'll hear a teacher say, can AI do and then they have this, like, two to three minute, like, really hyper personal Hyper specific.

Yeah. Example of, like, can this do this in my classroom? And I'm I always say, I don't know. Ask the AI.

Yeah. It will tell you whether or not it can do it. And if it does do it, then it's just gonna do it, and you're gonna have that thing done. And then you can retool it if you want to.

If it can't, it's just gonna, you know, give it a couple months. It might be able to do it then. And so I always encourage teachers to think that through.

But then once we get through creativity, you have to start thinking about inclusivity and how AI can act as a tool to meet the needs of students who are traditionally underserved and helping them to work with it. I think about my my my role in the classroom when I was in middle school, high school, and I was always the kid who was smart enough to get work done quick, and I would always have to tutor someone else. And that's not an effective use of my time. It's it's just the literature shows us that.

If I had AI that would extend my learning and encourage me to do really interesting things, it may have been a different scenario for me. And so we gotta think about our gifted and talented, our multilingual learners, our students with disabilities. How can AI be leveraged to help support those communities in ways that currently we're not able to. So I I really like walking that through with teachers and showing them that AI is not just this amazing tool that can write my lesson plan. It's it's actually can do some really heavy lifting with us to build our classrooms to where we wanna be.

Yeah. And that idea that that doesn't undermine the value of an educator or in any way to track. It just it just powers them in new and exciting ways that makes the learning more engaging, makes it more personalized. Right?

Like, that is I love that viewpoint. There was a really good report from the Digital Education Council, towards the end of last year that actually said, okay. Sixty one percent of educators, are actually using AI. Like, they've they've done something with AI.

Right? The bar was pretty low. But that meant forty percent, you know, not not too shy of half hadn't. Right?

So so what does this kind of shift with AI mean for faculty?

Are they learning these tools? I I also have a weird kind of bias because I go out and talk to universities, and I tend to talk to the ones that are using AI most aggressively.

And so I'm I'm a little biased in how much I think people are using these. But what is what is the teacher's role using the AI tools? How does that evolve? You know? I think that's a a lot of people's questions.

So, you know, one thing which Google came out, there's a blog from Google, which was kind of shocking, and I've forgotten the exact numbers, but they sort of went out, surveyed all the users, not just education users, right, for AI, and actually it's turned out that the super users were both educators and students, right? So very, very Now students are using more, but it was maybe off by more than ten points or something, right?

I do believe that in one place, all of us started using AI, especially the first generation of it, just as productivity tools, right?

Just go out there, let me help me create my lesson plans, my assessment, my study Rubrics, yeah, guides, yep.

Yeah, rubrics and study guides. So I think that sort of happened, right? But I do believe that all the disruptions, fun, if it stays with productivity improvement, that's only so interesting, though, yeah, at least in case of education, I'm optimistic that AI will help the educators deliver what I think has been the holy grail. It's like personalization, right?

So for example, in the last change I was visiting one of, I was really surprised to see, like in a computer science department, maybe I shouldn't have been that surprised, but it's a few hundred percent class, and the teacher has already moved to a flipped classroom. So that was kind of the first attempt of personalization, right? So the student do all the videos at whatever in their dorm rooms, come there, she will go out there, have these groups of four or five students all through the classroom working on a project or whatever, and she and her teachers are trying to sort of help as needed. So I'm hoping that the superpower AI will give to the educators now is to do the personalization at the next level, right? So just as a personal story, now my grandson was like fourteen years old, when I had the chance, I and friends saw that he joined a school called Fusion School. I don't know if you guys have heard of it.

Oh yeah, yeah, They are a user, right?

Yeah.

And I love the fact that it's everything, it's one on one, right? Yeah, my grandson is thriving in it, and I just and that's what obviously, is to scale this up, there's a cost issue, but And so at least going back to your question about educators, I'm hoping AI is the one just gets them closer, right, to deliver personalization, exactly what tools they use, and we are experimenting with that. I'm sure others are too.

Well, I think I think that's one thing that gets lost a lot too is the idea that you don't have to be a graphic designer. You don't have to a videographer. You can create tools that help engagement in your courses in ways that you've never been able to do before, and I think that gets lost a lot in this as well. So yeah.

Well and and I wanna, hold on to this idea. So so personalization excites you. What else excites you, you know, next five to ten years ahead around what AI can bring to either computer science education or education in general or both?

So computer science, I mean, generally right now, it excites me to be in this moment, even though there's all this doom and gloom around it, right? I mean, because, personally, I don't think it's a Model T moment, right? It's not like, Oh no, we have to start worrying about all the people who are caring about the horses and drive carriages, like what will happen to them, right? I do think it's one of those moments, more like a calculator moment, or a little bit more boring is that you came up with a higher level language in computer programming.

In some ways, programming was due for a disruption. We have been doing the same thing like thirty years, when we went from assembly like code to higher level programming. So I do think what excites me right now that the computer science is going to, in various forms, is going to start touching every part of our society, either maybe in terms of physical AI, in terms of going I'm hoping Like, data science was interesting, right? A few years ago, there were no data science programs.

Their data science programs, their people are building colleges because that's an intersection of computer science and say math, statistics, right?

Now I'm hoping that, you know, computer science will see maybe, you know, at the intersection of maybe learning. Wouldn't it be amazing that if we start seeing now learning science and AI in computer science becoming this big sort of area of focus because finally, in all the stuff that we have been learning and learning science, you can actually deliver So it comes back to personalization, but driven by computer science.

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