Read the full transcript of Episode 81 of the NAIS New View EDU podcast, which features Peter Nilsson, co-author of the forthcoming book Irreplaceable: How AI Changes Everything and Nothing about Teaching and Learning. He joins host Morva McDonald to share what has changed because of artificial intelligence (AI), what hasn’t, and how his work using technology to bolster innovation in education led him to this place.
Morva McDonald: Peter Nilsson is the co-author of the forthcoming book: Irreplaceable: How AI Changes Everything (& Nothing) about Teaching and Learning. From tutor to English teacher to head of school, he has served in a variety of roles in schools. He is the founder of Athena Lab and the editor of the weekly newsletter The Educator’s Notebook.
Over a year ago, I had a great conversation with Peter about Athena Lab and the importance of supporting the development of professional knowledge in education. The goal is that we don’t need to reinvent the wheel every time we’re in a school or in a classroom. Since my time as a doctoral student at Stanford, where I was connected to some of the best thinkers related to teacher knowledge and expertise, I have always been committed to supporting the field of education to be viewed as a professional field. I greatly appreciate Peter’s dedication in this area as well and can’t wait to learn about what he’s thinking about now.
Hi, Peter. I'm super excited to talk to you. It's been maybe a little over a year, I think, since we had such a really lengthy, interesting conversation. I always like to start with a question that helps the audience understand who you are and how you got to care about the things you care about. So tell us a little bit about your journey to becoming an educator committed to schools, really, and also to helping schools right now engage AI.
Peter Nilsson: Thanks, Morva, so much. It's great to chat with you again, too, I'm really looking forward to the conversation ahead.
I wandered a little bit into teaching. After college, for a year, I worked at the college for a year, writing music, doing other things. I had a residential life job, but I was writing music and doing other things. And then I said, having gone to an independent school and having learned about teaching fellowships, I said, I think I'll try a teaching fellowship for a year. And after those two years, I'll see which one I like more.
And going into the classroom was an immersive learning experience and I quickly learned that, whatever you think teaching is, if you've never been one, once you are a teacher, it is something very different. It is, especially at independent schools where you are teaching, coaching, and at boarding schools, living in dorms. I found the environment just so intellectually, socially, artistically stimulating as I was able to, to bring all of who I am to the school where I was teaching. One year quickly turned into two, turned into three, turned into five as I was discovering all of the joys and challenges and richness that make up education.
That's also true about leadership, school leadership, whatever you think it is as a teacher, when you step into it, it's different. Headship in particular. And now serving on the board of a school, same kind of thing. You think it's one thing, but then you step into it. And what has been fascinating just to, to, to pick on, not to pick on, but to tag the end of your question about AI and the other work that, that we've talked about before.
What's so interesting is because it is so rich and so complex, there are so many angles into it. And AI is its own discipline and domain. Different other technology tools are their own disciplines and domains. And when you have been inside the classroom and understand the richness of what makes education, then as you start to learn about other ways of being, other entities, other technologies, other organizations and groups, and you can begin to see how they all fit together to make a very complex puzzle.
Morva McDonald: I'd love to hear a little bit about, I mean, I know that you were the founder of Athena Lab. Some kind of, you know, Wikipedia meets Facebook kind of thing for teachers. Tell us a little bit about Athena Labs to start and what problem, right? What's the wicked problem that you think Athena Lab is trying to help us pay attention to, solve, work on, right? As it relates to teaching and learning?
Peter Nilsson: Yeah. Yeah. So that, the realization of the problem happened while I was in graduate school, where I had this realization that the field of education has no professional memory. These were the early days of Wikipedia, the early days of Facebook. And what I was observing at the time was that doctors have thousands of years of practices indexed and recorded for diagnosis and treatment that they can all refer to. And they study in medical school.
Lawyers have hundreds of years of case history that they can all refer to. It's a shared knowledge base across the legal profession. But teachers, we reinvent the wheel almost every day that we're in the classroom. And I remember my first five years of teaching, sitting in the department office every day and saying, what do you do for “The Knight's Tale” in The Canterbury Tales? What do you do for Beloved? What do you do for Phillis Wheatley? What do you do for The Great Gatsby?
There was not even a file cabinet. I lobbied the department chair for a file cabinet and then I just started collecting files and putting them in folders sorted by the texts and the topics that we were teaching. And that was, of course, those are early internet days, 2000, 2001, early internet bubble, I should note. So before even, you know, web 2.0 had come around. So by ‘05, ’06, '07, when I was in New York City, early days of Facebook and Wikipedia, I said, there's got to be a Facebook meets Wikipedia for teachers. There's got to be a shared knowledge base and a shared collaboration space for teachers to build this, this collection of practices so that we can draw on each other's expertise the way doctors and lawyers do.
It turns out this itself is its own, if not a wicked problem, its own very complex problem, because unlike medicine and unlike law, education is diverse in the way that it is applied in different classrooms. There isn't only one way to teach The Great Gatsby. There isn't only one way to teach Beloved. In fact, every classroom should be different in the way that it engages it because every classroom has different students. So while knowledge on Wikipedia compiles everybody's contributions to the page on physics compiled to one page, curriculum does the opposite.
Curriculum doesn't compile. It disaggregates. It diversifies. And so the challenge of solving this particular problem is finding a way to organize information in a way that integrates diversity of practices in a user experience that is intuitive. And this is what we began conceiving those many years ago, and began building and engaging with teachers over the last decade or so.
Morva McDonald: It's interesting, you reminded me of something. A lot of teaching sometimes is conceptualized as like letting a thousand flowers bloom, right? Because it's so different. Every classroom is different. Every class of students, even, is different, even as the same context, right? But then that highlights this tension of then, but what is consistent? What is similar, right? In the teaching of English, right? In a high school, in 10th grade, regardless to some point, to some extent, right? About the context.
And so talk to me a little bit about one, that tension, and also in my own experience, the tendency of things like Athena Lab or other things that are similar to really focus in on really practical skills for teachers about their pedagogy, how to organize activities, right? Very activity-based. So how's Athena the same or different, right, from something like that?
Peter Nilsson: Great question.
The question, as I hear at the heart of it, is a question about a tension that is the individual versus the group, consistency versus autonomy, general principles as they apply to a classroom versus individual customization for a space.
Morva McDonald: Yeah, the perennial challenges in the field, right? These are perennial problems. They're everywhere. They're consistent.
Peter Nilsson: And it's fascinating because like the research will show us that you're right. There are consistent practices and we know that at elementary school grade levels, for example, that phonics is necessary for effective literacy. We know that quantifying objects, you know, I forget the word right now, but that helps with building numeracy at the elementary school levels. Like there are best ways to do that.
Of course, how you implement that best way can differ from classroom to classroom. Are you giving people 10 beads to get, you know, elementary school kids 10 beads to count, or are you giving them 10 baseballs to count or whatever else it might be? Are you asking kids to practice phonics on words related to subject A or words related to subject B?
So there is research that does explore commonalities and show that there are things that in principle ought to be consistent. But as I was just hearing the other day in a playwriting class, it's like in architecture, the laws of physics for how you design a house are consistent. I should say, rather you're designing, whether you're designing a house or a skyscraper, the laws of physics are the same. But how we do it from house to house and how we do it from building to building of different sizes, shapes and purposes, differs.
Within that, there are principles and practices in education that are consistent. The learning science principles that really started coming into vogue, I would say about 15 years ago, cognitive science of learning really started to aggregate this, psychologists and cognitive scientists started to aggregate knowledge in ways that were becoming more practical. That knowledge is consistent about how the brain operates and yet, every human being from the moment we're born and even from before the moment we're born is building different emotional, chemical, psychological experiences. And the prior knowledge that we're developing over the course of our lives changes the way that we interpret information. Those differences only grow larger as we get older. And so therefore the need to customize more and more only also gets larger as we grow, we grow more and more. That tension will always be there.
It's hard to live in the middle of those tensions and it's easy to stake out a claim and say, research says we should do this. So we should script every curriculum. That's easy to say and ignores the nuance of the profession. It's also easy to say, I'm an expert. I know my classroom. I should have radical autonomy, but that also misses the nuance of the fact that there is research out there that can be helpful and should be helpful to all practitioners.
Morva McDonald: I think this is the heart of something that's really important, which is the fundamental experience in teaching and leadership. I would say those are similar in the world of education around dilemmas, right? It's managing dilemmas. This is a concept from many, many years ago, right? About managing dilemmas inside of teaching and that there is no particular right answer, but there are paths that you can choose along the way.
Talk to me, though, about why the development of professional memory or professional knowledge is helpful here and the extent to which it furthers, I think, in some of your prior discussions with me, kind of furthers innovation. What's the relationship between that and innovation?
Peter Nilsson: Professional knowledge is essential and I'll try not to get ahead to the question of innovation.
I would say it's near impossible for any one teacher to know all the right things to do day after day after day and to develop whole cloth, expert work day after day after day without building up a career of experience to do that and to lean on previous knowledge. The professional knowledge base is important. Before I get to that, how did we solve this in the past?
You would observe the veteran teachers in your department. You would share your curriculum with each other. You would, if you were lucky, you had colleagues who could do that. Many colleagues would exist at schools where this didn't exist, or, they were the only person teaching their particular course. There was no knowledge passed down. So we did this before, or when we could, we did this before. We built file cabinets and we shared things in those file cabinets. But there's still so much for teachers to do day in and day out.
And if we are able to continue growing a shared knowledge base, then what it means is we can find those teachers and the work of those teachers whose work looks like ours, whose needs look like ours, so that we don't have to create whole cloth, these new ideas. Why is that so important? That's so important because our teaching is not just teaching the Great Gatsby or Beloved or Phyllis Wheatley or Shakespeare or whatever else it might be. Our teaching has to evolve year in and year out as we connect these, what are these tried and true subject matters that are important, whether it's grammar or whether it is physics and momentum, or whether it is biology or whether it is language. We have to connect these to students who are ever changing.
So therefore the way we teach it must ever change, and we have to connect it to external contexts that are ever changing. And the way that, the ways in which we connect our learning to our students' life, that also evolves, as their lives change and as the world changes. So, so the ability to do that is harder and harder as the world changes faster and faster. And if we are connected to communities of educators who are sharing the ways in which they're doing things, then we have the ability to be more responsive to a changing world. We have the ability to improve more easily as we're exposed to new different ideas and as we identify those ideas that work well for our classroom and that we can then adjust and adapt for our class spaces, for our students.
Morva McDonald: I'm going to ask you to help me make a connection here, right? So most of the audience, right, of New View EDU tends to be educational leaders, right? They're leaders in schools, maybe they're heads of schools, maybe they're department chairs. They can be all kinds of people, right? But they're in leadership roles.
So help me make a connection about how this conversation about professional knowledge and memory has anything to do, right, with Heads of school. Having been a Head of school, how did you think about that? Like my own answers, having been a Head of school, about how I would think about that internally to my building, but in response, but sometimes it's helpful to be more explicit, right? About what's the relationship here between this notion, right? And my work as a leader in a particular institution.
Peter Nilsson: Yeah. So I'm going to try to go out at this from two angles. One is again, that historical timeless approach, and two in this particular moment. One, that historical timeless approach, is even 20 years ago, more than 90% of teachers in those early days of the internet were Googling to find online. Can I find lessons online? And now it's like 98% of teachers are looking online to find things, and the quality that's out there is not great, but that is showing that there is a need that your teachers have for new materials and for ideas to help them be the best that they can be.
And especially in independent schools where we are offering a value proposition, a market-based value proposition, our teachers in independent schools, and I say this now from a board position being on the board of a school, our teachers need to be the best. They need to be cutting edge. They need to be, or more transactionally for parents who are paying enormous sums of money for most of these schools, our parents – or even modest sums of money, the fact is they're paying. There needs to be, from a leadership perspective, a return on the parents' investment in their children's education, and in order for us to ensure that our teachers are as high quality as can be, especially in a time when teacher turnover may be accelerating and the teacher's supply pool is getting smaller, the quality of our teachers is the thing that defines us to parents in the landscape that they are evaluating.
So, from a timeless perspective, knowing that teachers are looking for this, they're spending gobs of money on Teachers Pay Teachers in many cases for low quality material in many cases, if there were a professional knowledge base that were shared and open, that would be that, that if, if they have access to it, that advances what we need as leaders, which is to ensure that we have the highest quality education that we can provide in today's specific context.
From an AI perspective, you know, looking at something like that, in a time when the world is changing and in a time when the demands and pressures on teachers and schools are, are greater and coming at us faster than before. We need to be able to be as responsive as possible. And now the research, interestingly, on innovation, especially on diffusion of innovations, is fascinating and actually surprising, you know, quite old, relative to the way we think about innovation.
Mid-20th century, Everett Rogers began doing research that later became his book, Diffusion of Innovations. And it was about farm, you know, farm workers and farm owners trying to figure out how to grow crops most effectively and most efficiently. And he studied the way innovations diffused across different states, across different networks of farm owners.
Morva McDonald: It’s like, the spreading of ideas, right?
Peter Nilsson: Exactly. It's the spreading of ideas. Of course, this is all hyper accelerated by the internet. Now we call it going viral. But again, there's this, there's this idea that practices what we need as times change our ways to respond to changing times.
It's impossible to expect every teacher, every school, even to be able to develop the wisest,
most effective responses to every change. That's just not how innovation happens. What happens is people all across networks figure out small little things. And the more those small little things can share across the network, the more any individual node on the network can have the most comprehensive, high quality, effective response to that thing. So therefore, if we can create spaces where our educators, where our school leaders, who are hungry to effectively respond to these changing times, AI, culture wars, you name it. They want, we want the tools to do that, the practices to do that, the granular, but not just the ideas and principles, but also what that actually looks like in my classroom tomorrow or in my leadership team meeting tomorrow.
If we can create that shared knowledge base, that professional memory, the easy means to connect teachers across that, then we can each, in our nodes, in this large network of schools, advance our practice and increase the quality as quickly as we possibly can by supporting people with the knowledge and the network that they need to do that.
As school leaders, we have an obligation, I believe, to create the space for the teachers and leaders to connect with each other, to share and diffuse these practices and gather and practice them so that they can bring them in the classroom. If we are not, there's fascinating research about this, a report called Time for Teachers, I think it's from 2014. If we are not building at least 30 hours, 30 hours of built-in practice-based professional development in which teachers are collaborating around what they do in the classroom, then we are not actually advancing the collective quality of what's happening in our schools. So that's basically an hour a week.
If there isn't one hour per week dedicated to improving practice, then we're not helping our teachers sufficiently. And we should not expect that practice is going to move across the board in service of student progress and performance.
Morva McDonald: Yeah, I'm in total agreement with that. I think that's super interesting. It's not just time. They need particular kinds of structures to engage with each other around the learning of practice, the learning in practice or learning practice in that work.
I'm really struck by some of the things you're saying, and I'm interested in how it connects actually to the title of your co-authored book, which is Irreplaceable, How AI Changes Everything and Nothing About Teaching and Learning. Of course, there is the core paradox in the title itself. What is the everything that AI changes? And more importantly, what is the nothing?
Peter Nilsson: You know, it, it's a great question and it's something that we came to very early on in the process, which started with the idea that everything is changing in that AI is the latest in a string of technological advancements that have changed schooling over thousands of years.
From the invention of writing to the personal computer to the internet, we have seen the experience of learning evolve. And yet through it all the skills that we've been teaching have been the same. Critical thinking, communication, collaboration, and others, like Socrates was teaching these same skills over 2000 years ago.
They're timeless. We are still teaching them today whether students are learning on parchment, or with computers, we have asked them to analyze. We as teachers have asked the students to analyze evidence, to creatively solve problems, to evaluate sources, to engage with complex ideas and more. With AI, it's the same thing.
AI is providing new tools, and now we are asking students to do the same thing. To evaluate those tools, to think creatively with them. With graphing calculator, before graphing calculators, they drew graphs by hand and asked questions about them. Now with graphing calculators, they draw graphs with a calculator and ask questions about it. Before AI, we wrote through iterations. Now with AI, we write in iterations. We are asking them to do the same skills, to think creatively, to find their own voice with these new tools. So in the level of technology itself, everything is changing. The technology and tools we're using. And nothing is changing. The skills remain the same.
But we explore this everything/nothing dichotomy in two additional ways. And one of those is on a real human level. And that just starts with that, while AI can personalize material, while it can create bespoke experiences that answer students' questions with imperfect, but improving reliability, and while AI is more human-like than ever. While AI can do these things, it can, it can bring students more self-directed learning experiences, and even now, the prospect of an AI tutor, like a personal AI tutor is arriving. That is changing the individual human experience. It's, it's a seductive vision, and it gives the impression of endless immediate learning.
But what AI can't ever do is perfectly optimize a learning experience. Humans are messy. It starts there. We take time. We're different. Acquiring knowledge and skills is different from downloading information. We are not in a matrix-like world where we can upload information. It's discursive, the fundamental biology and neuroscience of what it means to learn doesn't change. That richly complex experience can't be sidestepped. It still takes time to accumulate wisdom. It still takes time to learn from experience. It still takes time to, to bring knowledge into our brains in ways that become automatic. Students now can do more, so much more than they ever could do before. Every student having something like this vision of an AI tutor is a game changer for so many reasons. But nonetheless, students will still need time. They will still need help. They will still need practice. They will still struggle to ask the right question. They will still come in confused about something. They will still need teachers to help them build confidence.
Everything is changing in terms of how we do this on a human, individual level where we're interacting with a machine that is more and more like a human, but nothing is changing in that the messiness of our own human learning remains. So that's the second, very human way.
The third human everything/nothing dichotomy is really about the grammar of school itself. Is that we outline in the book three different contexts of school, with teachers alone outside of class planning and working, students alone outside of class doing work, and then teachers and students together inside of class.
AI will and already is supporting all three of these areas, teachers with AI assistants, students with AI tutors, classrooms with AI tools. These assistants and tools are changing seemingly everything about school and learning, and it is certainly changing across these areas. But the grammar of those three areas remains. Nothing is changing there.
Students will learn more and more outside of class, but they will always return to class to make meaning. The classroom is where our ideas are tested in the world, where we viscerally experience different perspectives, where we perform labs, where we question each other about how to understand and find significance in history or math or literature or language. It's where our perspectives come up against others.
We will always need this grammar of schooling. No matter how much techno evangelists try to paint a picture of students learning fully from an AI tutor, they will need to come back to the classroom to make sense of it, to make meaning of it. It's necessary not just for the ideal of a diverse and civil society where everybody understands perspectives more, but more simply, it's necessary because kids will have questions they'll want or need to ask other people in that messy learning experience.
Morva McDonald: One of the things I've heard recently from somebody that I thought was really compelling was like, AI will do lots of things related to teaching and learning. But one of things that is fundamental about teaching and learning, which I think is related to what you're saying, is that it is a human endeavor.
It's very deeply human, it's very deeply social endeavor, right? And we know this from lots of things, all kinds of learning theory about the social nature of learning itself, right? So help me make the link between your answer, that I view to be quite about the individual change, changing something or nothing, right? And the change that it may or may not have related to teaching and learning or structures of schooling, related to kids.
Peter Nilsson: Great, thanks. Perfect. You may not know this, but it's the perfect introduction to the structure of our book. We begin with the organization of three contexts of the book into three contexts of learning. Teachers alone preparing, or in groups, preparing for classes. Students alone, or in groups, doing work. And then teachers and students together working in the classroom, whatever that classroom might be.
Those three structures system-wide, collectively, collaboratively, not individually, those three structures are part of what we also say is irreplaceable. That's going to be happening. Now what's happening in each of those three spaces is different. And we argue that AI can play roles in all three of those spaces. For teachers, AI is a research assistant, it is a planning assistant, and it is a feedback assistant.
For students, AI is a learning assistant and a doing assistant. And then in the classroom, it is an administrative assistant and it is a teaching assistant. It's an administrative assistant for logistics in the classroom and a teaching assistant to help with the actual teaching. And what we do in the book is we explore the research that exists for all of those different seven environments, the three contexts, but seven chapters.
We have one chapter for each of those assistants. Part one is about teachers alone, part two is students alone, part three is teachers and students together in the classroom. We explore research in each of those three areas and then we layer on matrix like, all right, how does the technology of AI play a role in what we know about what makes for good planning, about the ways in which teachers research, about what we know about how students learn, about what we know about how students do and the relationship between learning and doing and how those two are innately connected. So system-wide, we're looking at that structure and how that structure is going to, will continue. But then what that looks like for each individual is going to be different. And that's where AI layers into the discussion.
Morva McDonald: Can you help me build an image of that through like an example of something particular? I'm trying to think about this out loud with you, Peter. Like what's an example, right, that the structure holds steady basically, but the nature of the work, I think is what you're saying a little bit will be different?
What is that? It's hard to see, right? This is a very conceptual idea that you're talking about. So I'm trying to ground it in something that's a little bit more specific or familiar.
Peter Nilsson: Yeah, yeah, yeah, yeah. So we could begin at any one of those three settings. Let's, we could almost think about the life cycle of a learning experience. When we think about, let's say research and planning for a teacher, where does a teacher generally begin their research work? And we define research to say, this is about content, this is about standards, and this is about students. Like standards or objectives. When I'm teaching sophomore English, what is the content I'm teaching? What are the objectives I'm trying to accomplish and who are my students?
Now AI can help facilitate my discovery of content, my understanding of content. It helps my learning and growth as a teacher. It can help me understand my objectives and it can, it can help me understand. It can provide insight and directions for exploration between how certain content that I love might serve particular objectives. And it can help me understand my students in a world in which our student information systems have language models built into them that protect their privacy. Then to be able to pull on a synthesis of all teacher comments for students that have come before, before I meet the student could be a powerful tool. Some people may say, no, it's going to predispose me in one way or another. Others may say, actually, this will help inform the way that I prepare for them.
Morva McDonald: It helps you aggregate the experience. Yeah, I can see that.
Peter Nilsson: That's just the research phase. So concretely, it's a tool that can help in those areas. In the planning phase, the way we explore this is we actually look at, we frame teachers as being the most creative people in the world. What do I mean by that? I mean the people we typically think of as creatives, as like marketing and creative designers and advertisement folks, they might spend three months working on a 30 second commercial with a team of people, but that's a team of people spending three months on a 30 second commercial.
Educators, every day we are providing five hours of learning experience. Multiply that over 180 days or 140 days in the school year, our creative productivity, leaps and bounds far beyond that. And so we look at research around the creative process. What is the creative process? And we synthesize a whole bunch of creative process frameworks to say, well, the creative process has five stages: incubation, exploration, perspiration, revision, and integration.
Incubation, how are we taking in information? That's actually the research phase that we talked about in chapter one. Exploration, this is like trying out ideas. What works here? What works there? Just putting things on the table. Perspiration, you've decided an outline, now you're writing the essay. You've decided your plan, now you're creating all the different components of it. Revision, all right, how can I improve this? What should be fixed? And then integration, what happens when I bring this into my life?
Now AI can support all five of those stages. How can it help us with that research we've talked about? How can it generate ideas, help us generate ideas for how we might approach this? How can it help us actually produce materials? When we start to break it down into these individual phases, then we can start to think much more tactically about the ways in which we are leveraging AI in the classroom.
Now I'm going to sort of jump over to students here for a second, very concretely. The student section begins with actually a fairly lengthy section exploring and describing the failure of technologies to support student learning in the past. When we look at Khan Academy, when they say Khan Academy has these effects, these positive effects towards growth, actually what most people don't know is that the studies that show that only measure those students who followed the guidelines for how Khan Academy should be used. And guess what? That's only 5% of the students who participate in the studies. So that means 95% of the cases aren't even in the data that is showing this progress, because students are not engaging with tools. Roughly the same ratio shown with massively open online courses, that only 5% of people were persisting with it in ways that it was designed and therefore 95% of people weren't learning.
So we start by exploring that, then we explore, as I mentioned before, how is this different because of the social emotional capacity? So therefore, are we entering a phase now where students have greater capacity to drive their own learning than they did before in what we have imagined historically as that kind of AI tutor? And we say, yes, there is the capacity for that, but this is something that is still developing. And it is likely as well that this, like other previous technologies, will not fully reach every student.
So what does this mean practically? This means that just as we saw the beginning of the flipped classroom, from having online videos. Now that didn't solve the problem, but it shifted the balance of what students could learn on their own more towards students being able to learn more on their own. That balance is now going to continue shifting. Not every student is going to be able to achieve mastery on their own with AI, but more students will.
And that then therefore is going to practically shift what we do in the classroom and how we then choose to teach what we teach, how we make meaning in the classroom as opposed to spending time clarifying, and or how we physicalize, how we socialize, how we bring that learning from the students being at home to the classroom space.
Morva McDonald: Yeah, I mean, your example reminds me of something I think that's really key and maybe is related to the statement you said about the short-term impact and the long-term impact on our misunderstanding of that, right, which is this very famous historical book, right, about education called Tinkering Towards Utopia, right, which is essentially, it's a little bit. So yes, we've shifted our thinking and then it shifts again, right, and the extent to which AI will be a fundamental big shift, right? Or not, I think we have yet to see. And maybe the point is that maybe over the long haul it will, but about these smaller things that we think are really important right now will be less important, right, as we move through it.
I also really appreciate, and I think it's really great that in the book, but also in this conversation, the focusing in on the roles or people in schools for which AI will support them in particular kinds of ways, right, the research around teaching, I think the aggregation of assessment information about kids, which is really hard in classrooms, very hard for teachers to do in the timeframes that they have. I’m like you, I agree, teachers make equally as many decisions, I think, as air traffic controllers on any given day. It's a massive job that's very spontaneous, right? And people are improvising all the time. So this kind of resource is really helpful.
Peter Nilsson: I would add to that that at the end of every chapter, this book is not just for teachers, though its primary audience is teachers, but every chapter finishes with next steps for teachers, next steps for school leaders, and next steps for technologists. What does what we're saying, what does what we're suggesting imply for these different groups, for these different stakeholders, and how can they take this knowledge and move forward with it?
And I would add since, since the listenership for this podcast is mostly leaders, that the book is something of a Trojan horse. It comes from the angle of AI, but it's really about the tried and true long-term, long-lasting principles of teaching and learning. And reading this book is meant to be as much about good pedagogy and exposing people to the ideas of good pedagogy, learning objectives, cognitive load, planning, which in independent schools especially, might be less present than they might be in other, often more structured environments.
So the book engages these long-term principles of good teaching and learning very intentionally so that while we're talking, if you are a school leader and you're thinking about this book, then this is an opportunity to talk about AI very practically, but also engage your teachers in conversations about learning objectives, about principles of good practice, about feedback and what makes for effective feedback.
Morva McDonald: I knew that we would have a lot to talk about. I think one of the things, though, that's really interesting is the way that you just highlighted something about the book, that at its core are the perennial knowledge, professional memory, right? That we have around teaching and learning, the core principles of good teaching, right?
And then the book doesn't move too far afield in that, in part because AI isn't going to move us too far afield from that either. I knew I would have a lot to learn with you in this conversation. I look forward to any other conversation we might have in the future, Peter. And I really, really appreciate your time today.
Peter Nilsson: Morva, this is such a delight. Thank you so much. I'm so grateful to be here and similarly, always appreciate our conversation.