Understand and Deliver: The Global Science of Learning Education Network on "The Science of Learning"

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 there. Welcome to Educast three thousand. I am your cohost, Melissa Lobel.

And I'm your cohost, Ryan Lufkin. And Melissa and I are very excited to introduce today's podcast guest, Kelly Shiohira. Kelly is the director at the Global Science of Learning Education Network. Kelly, welcome to the podcast.

Thanks. It's such a pleasure to be here.

Well, before we jump in, you have a fascinating career. In education, policy, and now neuroscience informed learning. Tell us a little bit about that journey because I think it's just amazing.

Oh, I'd love to talk about this journey. Yes. So so prior to joining the Global Science of Learning Education Network, I was the head of the research and data ecosystems division at a nonprofit education consultancy based in Africa called Jet Education Services. Over a fairly long career in South Africa mostly, but also kind of across the continent and some international work, I saw two things happen.

You know, we evaluated dozens, if not hundreds of projects and helped with interventions, always aimed at improving things for the most vulnerable. Yeah. Very few of those projects actually did what I hoped they would do. They were all all meaningful forms, some very well funded.

But in the end, you kind of saw two things tend to happen. One was statistically significant results that researchers were really excited about, but my practical nature was less excited about because, you know, we'd spent three million dollars so that kids learned another eight letters of the alphabet or something like this in practice. So just not enough to get up to grade level standards or make a meaningful difference in the lives of the children. And then the other thing that we saw quite a lot was very well resourced, well thought out, well funded projects that just couldn't be adopted by systems because it is a constraints first type of environment.

And I kind of had an epiphany moment when I was on stage with a certain someone who will not be named, who made the statement about the fact that seventy percent of the world's children wouldn't meet kind of global standard targets by twenty thirty. I remember thinking to myself, why does that matter? Because seventy percent of the world's children are not in that environment, you know? So I did this thought exercise with a few members of my team where we looked at what would an education system actually look like or need to look like in order to really serve the needs of people in these communities and talk to teachers about it.

And the answer is, you know, it wasn't a revelation. Let me put it that way. Like, it made perfect sense. Teachers were telling us they needed more time to build relationships with their kids.

And actually, these kids didn't come from trusting homes with, like, supportive adults, caring communities. They were in high risk environments, so they needed time to build those relationships with teachers. And that was actually the more important job than the curriculum. But yet the system was telling teachers, your most important job is the curriculum, and you're not delivering the curriculum well enough.

So we need to fix you because you're broken. But, actually, the teachers were right. You know, instinctively and intuitively, they realized that actually kids weren't gonna learn anything unless they spent that time. And the curriculum was too fast, and they needed more space.

They didn't come from pre learning environments where early early childhood education was really robust. So all of that kind of happened mentally in my space at the same time that I was introduced to this network, you know, the Global Signs of Learning Education Network. And what it is is essentially with a step back that I think education needs. Like, let's actually think about how learning happens.

The fact that we've built all of our systems in education based on observations of a very select group. Think of who Piaget, Dewey, Bayagotsky, who were these people actually looking at? And let's think about actually how the brain works and how the constant among us is, you know, neurological processes remain fairly, fairly constant. There's individual variation, but there's some things that we know now and we can build from.

And all children are learning all the time, you know. So that's really what the network is all about. How can we help education systems structure themselves more around learning and less around outcomes and less around standards and benchmarks and see if we can finally crack the code that so many of us have been working so diligently on for decades and not getting anywhere.

That's amazing.

Yeah. So how long have you been with the network?

Not very long. A year and a half.

That's amazing. No. That's long. That's great. That's fantastic. Those epiphany moments in life, I can really relate to those.

I changed roles at Instructure about a year and a half ago. And it was one of those moments for me too of like, oh, this is how, you know, you're exposed enough to the world around you and you're like, there's a different way to make a difference here and we can absolutely do that. And speaking of those epiphany moments, one of the questions we always ask our guests is a favorite learning moment in your life. And so it could be one where you were the learner.

It could be one where you were the teacher. It could be something you observed. And you've kind of described one already on stage, right? And having this moment in your own career journey, but perhaps maybe share with us a personal learning moment or a favorite learning moment.

So I think my favorite and most important learning moment, I would say there are two. And one was Great. Was learning to fail, actually.

Yeah.

Love this. I've always I've always been really bad at failing at things. I never really thought of that as a skill. And then I realized at some point, you know, mid twenties maybe, I realized that I was not doing things because of fear of failure.

Oh, yes. You know, like I was not trying for that contest or that competition or I was not entering that thing or I wasn't going for that position because I was like, oh, I might not get it and I was afraid to fail. And so at the time, was working in a high school in Japan and they had a judo club. And so this kind of, again, a very fortuitous confluence of events.

Was that I got really fat. Yes.

Like, I thought Japan was this healthy culture and moved to Japan and I was like But their food is so eating whatever I want.

And it turns out that if you stop and eat chicken nanban, which is fried chicken covered in mayonnaise sauce for like four meals out of a week, you do gain quite a lot of weight. Like, I don't know.

So, you know, they operate in kilograms and I was used to pounds and so I saw the scale creeping up but like it didn't look that bad.

Until my mom came to visit and her first comment to me was like, wow. You have really put on some weight. I thought, okay. I need to do something about this.

Leave it to mom. Yeah.

So I took a tour of the school sports teams, and I thought this is, you know, good way for me to get closer to kids, learn some more Japanese, and and maybe get this whole scale issue under control a bit more. And at the same time, I had been realizing this about myself. Like, this is a flaw that I need to deal with. And I walked into the judo do jo, and I watched the kids, and they were so in control of their own bodies.

It was something I just there was something amazing about it right from the beginning. And then I was talking to the coach, and the thing he said was, if you join the judo team, you are going to fail for years. And I thought, this is it. Immersion therapy.

Exactly.

Oh my gosh. So That's crazy. So I joined judo, and that was just, yeah, a really, really important moment in my life. I learned a lot. And one of the things I did learn was how to take a fall, you know, like, literally and figuratively, and get up and keep going.

So On a broader sense, that is so important that we have that capacity.

Right? The fall down seven times, stand at eight. I think we don't always open ourselves up to the falling down those times that help us actually build the knowledge and build that skill. That's amazing.

Yeah. Yeah. Yeah. Definitely. And the second one I wanna share, it's much faster, is that, I learned that I wasn't right about everything also in Japan because I kept getting sick.

I was sick all the time. And part of that was moving to a new country. But what everyone around me kept saying was, you sleep with the air conditioner. And I was like, that's silly.

It doesn't have anything to do with that. I know germ theory. You guys are crazy. And then finally, like year three, I decided to try it.

And they were absolutely right. I can't. I stopped getting sick when I stopped sleeping with the air conditioner on. It is Yeah.

You know, I live my whole I'm from Florida. So we sleep with the air conditioner. Yeah.

But, you know, sometimes you just have to open yourself up to the possibility.

I've actually had that argument with my brother-in-law who was a doctor, and he's like he's like, no germs are there. I'm like, I don't know. I get a chill, and then I get sick. I just it's how it is.

So I'm sure there's some scientific basis to it.

Right?

Like the air conditioning filter isn't that clean and it's a totally unrelated podcast.

It's of I don't know.

I'm sure there's something. But it but it did teach to it.

Yes. Yeah.

Yeah.

It's it's absolutely I love those two learning lessons.

I can think at times in my life too where I think I've had to learn both of those. And it just, it grounds this idea that, and you were talking about this with the network, that what we think or how we should be learning that curriculum, those outcomes, aren't always the most important lessons of what's happening in our lives that can really shape our lives.

I know the network is really focused on sort of bridging the gap between how people learn and what's actually happening in the classroom. Maybe you could share like some examples or some of the work that you all have done just to ground our listeners in some of the sort of applications of that concept that the network is really trying to reinforce.

Yeah. So we are a network. So the way that we work is we have individual members and organizational members and other networks who are part of us. So it makes things a little bit messy in terms of accreditation, I suppose, for who's done what.

But the fact is as a network, we just believe we all do more together. And so some of the examples I'm sharing are network member initiatives. So I'll throw out some names. You guys can also look into them more.

I think one of the greatest examples of this in practice is at the Center for Transformative Teaching and Learning. And, essentially, what they've done, I really adore their process because they have a head of research whose job is to sort through all of the kind of scientific articles and things that come up and determine what's most relevant to teachers, and then they workshop with their teachers the applications of those articles and and what happens. And so they are a very privileged environment. They're at Saint Andrew's Episcopal School, which if you know schools in America, it's one of the most expensive in the country.

So they have the leeway to do that. But what's also great about them is that they're very willing to share and adapt. So they have a partnership with the Frederick County School District, and they've been working on implementing the science of learning there as well. And there's some newspaper articles and things written about that district and the effects that it's had.

And you may know Maryland has recently included the science of learning as one of the four explicit pillars of professional development that they want for teachers. And what this looks like in practice, you know, this is the other thing I tell people is that it's actually not it doesn't have to be dramatic shifts in practice, you know. So it's not that education has gotten everything wrong. When you talk about observations, you're observing what's actually happening, and you may not understand or know the underlying mechanisms.

But if you consistently observe enough over time, you're gonna be close to, if not on the mark. But some really important shifts that they've made that I would say are, again, not going to radically change, you know, what teachers have learned or what they have to do. But for example, they don't do revision sessions for students who fail. They do spark sessions for students they think are likely to fail.

Right? And so these are special invitation lessons where they pre learn the content so that they can succeed. So then they don't have the negative associations with learning that you might have if you fail something and then have to catch up. So, again, revolutionary, but something that everybody could do tomorrow, actually.

Are they using data to to identify those students that may not need But, you know, teachers know this stuff.

Like, if after a couple of weeks in the class or even the first round of material, they're gonna know who in the class is gonna need some extra attention. You know, kids enjoy being invited to those sessions. Like, you can make it special for them. You can know they'll treat whatever it takes.

So that's one thing. Another thing is handing out the review material in advance. So it's just almost changing the framing of, like, revision. And instead of, like, cramming at the end of a session right before the test, it's this periodic thing that you stretch over time, and revision is an ongoing process, which is really more aligned to how learning is.

Make a lot of sense.

Like, you're saying, this is not like as groundbreaking as like, you know Right.

So when I talk science of learning, I think people get scared or they hear science of math and they think, oh, no. They're gonna throw out everything I ever knew and I'm trying to have to change everything and get a whole new degree. I don't think it's that dramatic always. But there are things that we could do that would better serve all learners if we really just thought about it.

And another example is is ChatGPT. I don't know if you guys have seen this article that came out recently around ChatGPT and your brain on ChatGPT, and it's essentially neuroimaging scans of writing, search engine writing, and then people who use LLMs to write. And they switched groups, and it's a two hundred page paper. I mean, it's a massive study, so I'm not going to go into all the detail.

But I think what's interesting and what they did find is is something that educators would have told you if you had asked them. Students using LLMs are doing work, but the work that they're doing is mainly what we would call the preliminary writing stage work of aggregating information, and that is work for your brain. So they're reading what Cachi PT writes, and they're using all the same neurological processes that you would use to kind of typically do that. But what they're not doing is the generative and synthesis work that a handwriter does.

And there's some Interesting.

Yeah. And so what this means for teachers is is that ChatGPT could be useful because we know that one of the main gaps that students encounter, especially lower income students, is the prior knowledge gap. Right? So students who come in with a lot of prior knowledge do better on the tasks than than students who don't have them.

So it's possible that ChatGPT, and I would love to see this study, could help to kind of catch those students up again in a preliminary sort of fashion. Like before the task, give them ChatGPT to synthesize the world's knowledge on the topic, and then level the playing field for the actual task itself. So I'm really excited about what this study has shown. There's some criticism about the methodology, and and that's an interesting space for us in the network as well because what the criticisms are that there's a low number of participants and a dropping number of participants actually over time in the study, and a lot of criticism has been leveled about that.

But the thing about brain science is that the n can be one. You know, a lot of what we know about language came from Broca's study of one person, you know, tan.

So That's exactly right.

An n of eighteen is actually really high.

And so people who are criticizing this are coming from an education research perspective and kind of a different epistemology and thinking, you know, oh, that's not very many people. What does that really say? When actually, when we talk about neurological studies, it's very significant.

You know, so we're It's so interesting to understand, like, the brain science behind it, because I think there's a lot of there's a lot of talk about how much we're losing that creative thinking side or the the different aspects that we, know, much like when the calculator came out and we were gonna lose our ability for critical thinking, you know, because we were no longer gonna have to memorize formulas and those sorts of things.

Like, I think it's interesting to look at the actual, like, brain science behind that, and then we can actually fill in those gaps to make sure we have that evidence to make sure we're not losing something. Right?

There's some fantastic work on this. I would definitely point you to Barbara Oakley's recent work. She actually looks at memory and AI, and she's trying to explain this phenomenon that we've seen essentially in highly developed countries where we'd seen IQ actually plateau and start to drop. Interesting.

And she does some really interesting thought work and actual brain science work around why AI and technology more generally might be influencing that trend. So I really recommend her and Mary Anne Wolf as the other one who, especially in the world of linguistics and language, has done quite a lot of work. In two thousand and eight, she published a book essentially about how the brain learns to read. And in it, she was like, it'll be interesting to see what technology will do.

And then I she spent the next ten years studying what has technology done. And she's come out with a second book just kind of demonstrating what technology has done.

So I recommend both of those to excellent read.

Fascinating.

Yeah. We'll make sure to link these in our show notes too for all of our listeners. So because I know our listeners are gonna wanna dive in deeper. And sorry, Ryan. Please continue.

No. No. Well, I think one of things that's so interesting is with your background, other than the fact that you're probably you've spent more time across different regions of the globe than anyone else in education that I know of. It's fascinating.

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. Tell us a little bit about that that effort, I think, because I think 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 now we're seeing individual states, individual schools use as their guidance.

Yeah. 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 Chun Miao for the opportunity. Essentially, so there's 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 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 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 a 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 school schoolers. And so I did a side by side comparison of how they approached issues, 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 access to disabled populations like blind, deaf, you know, these types of, like, 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.

So and again, this was just looking at one grade, so it's possible these were covered elsewhere. But that was really fascinating. And then 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. Right? And that was a project. So just a 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 UNESCR project is that FengTun in particular led the charge to say, well, let's come up with an alternative. You know, but we don't like this.

If we don't like this, could think 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 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 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?

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 ChatuchuBT and asked it when to take my sleeping pill. And I wrote back like, come on, man.

Yeah. You can absolutely that is not a tough calculation.

No. That is Yeah.

You can do that. I know you. You have a PhD. Like Yeah.

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. Like, use it for things that you need it for.

Yes. That's all of it.

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 the the AI gender divide.

Amazing.

So interesting. Continuing on these frameworks. So, you know, 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, but where, you know, how has that how has the community reacted to having those distinct sets of frameworks, but at the same time, again, they're very complimentary?

Yeah. Yeah. So I would really recommend talking to Mutlu Kukurova if you haven't already. 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, 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. So actually the first two strands, the human centered piece and the ethics piece are in both frameworks. So there is a common grounding there.

And there is also an argument that teachers are students and they should all you know, I mean, the student framework lays out the AI skills we really think everybody should have. So 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. I 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 can do.

Yes. But yes.

But we do wanna at least give them the skills to be able to evaluate the technologies that they're using meaningfully. Right? So so the teacher framework covers things like assessment, for example, and pedagogy. So, like, 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. And so 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. You know? So you really have to think of this as a cross curricular type of exercise unless you wanna 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 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.

Yeah. We talk a lot about you know, we've had a number of podcast guests talk about AI over the last couple of years, and there are a lot of challenges, but also opportunities around, you know, getting educators and students prepared for AI and the future of AI. What, from your perspective, are kind of the biggest challenges, and and what are some of the opportunities that we can lean into?

The biggest challenges of artificial intelligence are definitely related to the workforce. And I'm going to say something really controversial right now, and I might be proven wrong. I almost hope so. But I think we spend a lot of time in education talking about preparing students for the future of work.

But I almost think that it's time to start preparing students for a future without work because How interesting. The reality is, to me, I'm a realist. You know? I'm a critical realist.

Yeah. So when I look at what's happening, the trends in automation, the number of jobs, and the number of tasks that can now be taken over with generative AI, even imperfect generative AI. You know, even when you tell a company, this is only right forty percent of the time, they still fire their grant writers.

Which is crazy. Yes.

Like, you were just they still fire their grant writers.

Yeah.

You know? Yeah. So when you look at kind of the motivations of industry, the ability of technology, and the human skill profile that we have. Like, yes, we can try to change the human skill profile to meet the needs of industry, but there just might not be as much demand. You know, that's just the absolute reality. Like, I mean, how many more people does it take to oversee one hundred AI producing bots? Whatever.

Versus, you know, ten. I I don't think we're gonna see this massive expansion kind of the supervisory technician class. Yeah. You know?

So I do think in some ways, the people who look to the past are right. Carl Frey looks at kind of what happens during technological revolutions, and I think we're very much in that space, you know, with the kind of increasing overall wealth, with the stagnation of the working class. Like, we've seen those trends. We know that is what's happening, but it just seems to be happening on an ongoing basis now.

Right? Like, it's not like we had one in eighteen fifty and then another one in nineteen hundred and then one in nineteen forty. It's it's pretty consistent now and I think likely to continue. So I also, at the same time, would point to the fact that historically, education has done pretty well in the industrialized world in the world of work.

Right? So we like to talk about how badly education prepares students for work, but then we also have unemployment that hovers around five percent. You know? So that indicator isn't looking too bad.

The indicators that are looking particularly bad in America are democratic participation indicators and health indicators. So all of the things around society, you know, the secondary becoming quickly secondary purposes of education, of civic responsibility, and building cultural participation, and things like that, we're not as strong, and we haven't been as strong at for a number of years now.

So So that's what I I mean, my real advice to education systems, which will probably not be taken for a while, is is, yeah, keep focusing on the world at work, but also think about what society is gonna need beyond just, you know, economic capacity skills.

Yeah. Yeah. That's fascinating. Yeah.

I could not agree more. I love that you've raised this. I also see this as a global phenomenon in at least in some parts of the world. How do you especially in you talked at the very beginning about some of the more underserved or under resourced areas of the world.

What are the opportunities with AI potentially there from either a challenge or more importantly, opportunity is? And do we even back to your point around reshaping of what we're preparing students for? Do we, you know, young people for? Is that completely different in other parts of the world that may not, you know, have the same kind of resources as the US does?

So interestingly, the kind of trends that we see in the industrial world and the, quote, unquote, third world or global south, what however you wanna define that, the lower income countries are likely to work in their favor for at least a while. Interesting. Because they are still very much manual focused societies where, like, huge parts of the industry are agriculture, their construction, their things that AI is infiltrating, but not to the point of they could replace low cost human labor. Right?

Yeah. And the technological barriers are and the social barriers are likely to be pretty significant in some parts of the world. And so I think we'll see this transition more rapidly in the industrialized world than the global south. And I think that will work in their favor for a while.

But the other thing is, you know, there's always talk about the possibility of leapfrogging or, you know, using technology in some way to catch up. So if you look at some of the work from economists like Ottore around the hollowing out thesis and the middle class kind of losing share, there is some potential or some have argued that AI has the potential to eliminate that. And I do think that there's something to that because if your barrier to entry is writing skill, Grammarly's got you covered, you know? If you need to make an email professional now, you could do that without actually having the skills to be grammatically perfect all the time.

And some of this we've had for a while, you know, autocorrect and spell check are not not new phenomenon, but the level to which it's been taken with things like Grammarly, I think, is is really significant. And you now do find new early career professionals who will admit to you if you ask them that they've never written a paper without their Yeah.

Yeah. Yeah.

And more power to them, you know? So that does lower the barriers to entry for some. And there is a potential that it can allow some of these kind of excluded middle skill individuals to enter higher skilled positions as we go forward. But I think the competition for higher skilled positions is also going to become steeper as we go on.

So that's something else to think about. What it means for the global south too is, you know, John Traxler will talk to you about digital extractive neocolonialism. And in a lot of ways, like, he's probably right. There's a hopeful optimistic view that I would love to hold that's like, yes, it will even the scales and we'll all be great.

But then there's a world right now in which there are some dominant tech players. It's a difficult industry to get into. There are high barriers, and they are operating from the global north. So I think we'll see some growth in China, in India, I think South Africa, Nigeria, Latin America.

I think we will see pockets where there's catch but I think we mustn't also leave behind Sierra Leone. Like, and it's much less likely there.

Yeah. Yeah. That's so interesting when you start thinking about how we can apply these frameworks on a global level. Right?

There really are those very different playing fields. Even though, you know, I've often said, oh, everyone started from the same playing field on, you know, November thirtieth twenty twenty two. There really is the the preexisting conditions really do have an impact as we get further down the road with this. But one of the things that I'm concerned about is, like, the digital citizenship aspect of this.

Like, you know, how do we make sure that AI is having a positive impact? I mean, in the US on our we're already seeing an impact for unfair elections and, you know, news and things like that. You know, what role can these frameworks play in helping provide some, I don't know, positive movement in that direction?

Yeah. I think it is One thing is the framework is a rallying point for reemphasizing things like citizenship. We actually talk about AI citizenship as one of the sixteen competency blocks in that framework.

And as far as I know, we were the first to talk about AI citizenship and what that means.

Yeah. And it was conversations that our group of experts had was you know? And it does actually like, industry's not wrong to talk about IP. Like, what is ownership in the world at AI?

And the thing about creating an a massive international framework like the UNESCO one is that we can't be as specific as you need to be at country level. You know, we're trying to cover not only the range of development, but also democratic versus nondemocratic and all of these different types of ideologies and worldviews. So what we can say about AI citizenship is that AI is changing the ways that citizens are expected to interact with each other, to develop themselves, to interact with government, and they're impacting the environment. And so what we really need countries to do, actually, and this is a big part of the work that we're embarking on now, is nationally to think about these four levels of individual impacts of AI, interpersonal impacts, societal impacts, and environmental impacts, and how they need to balance and define those within their country Yes.

Yeah. Maybe as one of our last questions, we can do a little looking ahead.

Just coming off of what you were It's the crystal ball.

We do. I think I depressed your learners. Don't my future looking ahead.

Mean, I have convinced myself that it's gonna be just like Wally, and we're just gonna be floating around And here we go. Air cushions, drinking our slushies, watching TV. Everything for us. I mean, it's impossible if we built the political will for universal basic income.

Mean, that would be a good first step. Yeah. No. It's so true. Get rid of the fear of socialism because we're gonna need it.

I mean, that's Yes. That these are interesting conversations that have to be had. Right? Absolutely. Like, if there were fewer jobs, if a lot of this is automated.

That's right. And we have all the money in the world. Do you know right now, if we evenly distributed the world's wealth among every person in the world, everybody would get fifty three thousand dollars and change. Right? Yeah. We'd all be middle income.

Yeah.

Yeah. If we do that. Well, we're not doing that. But I mean, I'm just saying there's enough money is my point.

Like, there's enough money. It's just where is the money and what is it doing? And what is it doing for us as a society? These are questions we need to answer.

Oh, absolutely. And it pulls right back into education because I think we often the thing I love about the UNESCO framework and this work, especially in context of some of the other things that we've seen, which are also equally valid. It's thinking long term, it's thinking true future. And I forget that when we have, you know, a six year old in our classroom, wherever it is around the world, we're not just shaping them for when they're eight, nine, or ten. We are shaping them for when they fifty.

And that I think is one of the dilemmas in my head around what can teachers do today, right? What can education systems do today to be thinking about the dramatic change that's happening tomorrow, but also think about the sustaining life impact. I mean, back to the, when you remember, one of your moments was learning to fail or it's how do we, I don't know, like looking, what advice do you have? How about that for teachers or for education systems around how do we plan for now and weigh into the future with this sort of uncertain and changing world?

So I think one of the positive things about AI entering the space is that we are finally having these conversations that have always been true. You know, the fact that it is impossible to prepare someone today for their future.

Like, it can't be done. For jobs Because we don't know what the future looks like.

Yeah. And, you know, no one saw the gig economy coming, and it came. You know? And so what do you do?

Like, what do you do with these huge social transitions? We're living longer than ever. We're not going to just have it done when you're eighteen. Like, okay.

You're done. Off you go. You're gonna be great for the rest of your life. So I think even more than before, to be fair, a lot of educators and education researchers and some policymakers were having this conversation already.

But even more than before, we're looking at lifelong learning as important idea to put in the minds of our youngest kids. Like, learning is the goal. Education is lifelong. You get a formal education.

We get the system of schools for a period of time. Make the most of it, you know? And I think we could do more to help kids do that. But we're not gonna prepare a six year old for when they're fifty.

You know? It's just at some point that the burden shifts onto the individual. You know? We can't maintain your entire life as this, like, formal education setting.

So people and teachers more and more, I think, are realizing that their job is to make sure kids love learning and wanna continue doing it as they go. And they're prepared to handle and resilient enough to handle the types of dramatic economic, social, environmental shifts that we're likely to see during their lifetime, their very long, hopefully, lifetimes. Yeah.

That's a great point. That's good point. Yeah. Well, this is we can honestly keep going forever.

Oh my gosh.

We are at our our designated time, but this is Kelly, this is fascinating. I really appreciate you sharing this with us. We we were looking forward to this podcast, and you did not disappoint with with your insights.

Yes. No. I'm I'm pleased to say that. I do just wanna say one thing that your listeners can do or a couple of things if they're connected to education systems.

As we went through the development of framework, we consulted, as I said, experts all over the world. But now with Instructure and others, we have this kind of informal coalition of organizations that are focused on getting student and teacher validation. So input from students and teachers internationally into the frameworks to tell us what they think, you know, what are student opinions? And you're the real experts on the ground doing the actual work.

So we very much welcome inputs and participation into this.

Awesome. And we'll provide some links, like I said, to those the frameworks, but then also we'll include links to the feedback.

Yeah. Surveys. Sure. That would be we would be so happy to get such a hopefully wide variety of people respond. Yeah.

Awesome. Awesome. Well, thank you, Kelly.

We will have you back in the not too distant future.

I have no doubt. Yes.

Yes. Thanks so much. I really, really enjoyed this conversation. And, I look forward to to talking to you again.

Thank you. Thank you.

Thanks for listening to this episode of Educast three thousand. Don't forget to like, subscribe, and drop us a review on your favorite podcast players so you don't miss an episode. If you have a topic you'd like us to explore more, please email us at InstructureCast at Instructure dot com, or you can drop us a line on any of the socials. You can find more contact info in the show notes. Thanks for listening, and we'll catch you on the next episode of Educast three thousand.