This is how kids should be learning with AI | Priya Lakhani

Twenty years ago, I founded a social enterprise. I wanted to change the world. And we were funding millions of meals to the underprivileged. We were providing tens of thousands of vaccines across parts of Africa. And we were funding schools in the slums of India. Now I thought that I was doing quite a good job and having a lot of impact in all of these areas, until one day I was working with ministers in the UK, and they said that 20 percent of students leave secondary schools in the UK and they're not able to read and write well enough. Now I thought with brick and mortar schools and qualified teachers, if they're not able to do that in the UK, then I'm not having the impact that I wanted to have in those schools, in the slums in India. So what's going on? What's the problem? We need to fix it. So I went to schools. I went to schools and I asked lots of questions. And I found two critical problems on the front line of education. The first is that they continue to have the one-size-fits-all delivery of education to a classroom of around 30 to 35 people. The second, I think you will agree with me, should be headline news every single day. 74 percent of teachers want to quit their jobs in the next three years. Why? It's because of workload. They spend so much time micro-marking, micro-assessing, trying to figure out where every child is at. They are teachers by day, and they are data analysts by night. And not one of them signed up to do that night job. So walking around schools, I had a smartphone in my hand, and we had machine-learning applications telling us how we should shop, how we should save and sleep. And I thought, why don't we have this technology in the classroom, telling us how we should learn? We’ve got to build that technology. But we can’t use any old machine learning recommendation engine. We need to combine artificial intelligence with neuroscientific theory and the learning sciences to learn how every single brain in this room learns. Because if we can fix learning, we can improve outcomes. We can personalize education for every single one of us and provide intelligent insights to teachers to reduce the workload. So 12 years ago, I built a team. They built the technology. It exists, students use it in over 140 countries. We've collected over 40 billion data points on how children learn. And I'm going to show you a couple of the things that I've learned about learning on the way. But before I do that, I thought it would be really important to share with you some student feedback that I have on our platform. It’s really important because it tells us what children’s expectations are when they use an AI education partner. So I get feedback like this. "I'm trying to say thank you." "It's lovely." "It's brilliant." "I think Century will help me achieve things that I thought were impossible." It's a golden child, right? My life's purpose has been fulfilled. And then these sweet, lovely, innocent children send me messages like this. “I don’t like this website, it makes me able to do my homework.” (Laughter) Wait. And then I'm being bribed. "I will give you 100,000 pounds, I'm not joking. You just need to give me no work. Give me a button to do the work for me." Now these children and that sentiment very much ties in with a recent survey where children were asked, how do you use AI LLMs, chatbots, with your homework? A staggering fifth of children admitted they get AI to do all of their work for them. So they're not using AI to help them learn. They're using AI to actively avoid learning. Now I know some of you are frowning right now thinking, how dare they? I don't think they're that different from us. Think about how we felt when we first used ChatGPT. This is my very scientific chart. I think that you all felt euphoric. You thought, "Wow, I'm going to look like a genius. I never need to do any work ever again. This is amazing." Yeah? And then it hallucinated and confabulated. And you were, like, "Big tech, seriously, you had one job to do, Sam Altman, with all that money, and it's making stuff up," right? And then for the lawyer who shared it in a courtroom and got fined, sheer humiliation and embarrassment for those people. And I think we've ended up with this sort of sinking realization of acceptance, right, that the shortcuts don't really replace the work. They're very helpful. But we still need to learn, we need to produce, and we need to think. Now when we read those long answers that an LLM chatbot gives us, it feels very fluent when you read it, doesn't it? The problem is, is that fluency we often mistake for learning, and that is why people we know, not us, of course, but they end up with this sort of illusion of competence, like they know everything, right? What we actually know about learning is that learning requires what researchers call a “productive struggle.” It's this sort of mental effort, right, that builds understanding. Now my top learning techniques, I’ve got four of them that all involve a productive struggle, and they improve outcomes. We've seen them work. Three of them are about memory. This is really important. Memory and understanding are two sides of the same coin. If you think about it, we draw on what we remember in order to shape what we think. If we can't recall it, we can't use it. So the first important one is retrieval. This is simply the act of recalling from our brains. The students in a study were given a passage like this one. And it's the students who only read it once, but then tried to recall it from their memory, who could remember it far better than students who just read it over and over and over again. The second is spacing, and this is essentially students who then space their learning over time. So rather than cramming things all in one go, students that can do that active process of retrieval over time, because then you're essentially going through that productive struggle over and over again. The third, we don't like this one, but it's just generation, right? So students in a study were given word pairs like rapid-fast and cold and hot. But then another set of students were just given the first word. And then a cue like the F, they had to come up with "fast." Students who have to generate the answers themselves, even if they get them wrong initially, create a stronger memory trace. They remember more in the end. And then the fourth is reflection. When we reflect on our work and we are given structured feedback in three very specific ways: How am I learning right now? What is my learning goal, and then what are the gaps to get to that goal, what do I need to do? Those students improve their outcomes. Now you'll find that these four techniques have something in common. They are harder. They all involve a productive struggle. We know sustained mental effort strengthens the parts of the brain, and it's positively correlated with growth in the brain. There was an amazing study in my home city of London with black taxi drivers. Now if you’re a cabbie in London, you have to pass a test called The Knowledge. You have to memorize 26,000 streets in the city of London. You're not allowed to use navigation apps. Wow, exactly, right? Isn't that crazy? Yeah, no Uber drivers for them, right? And so neuroscientists scanned their brains and they found that parts of the hippocampus in the brain, this is the part of the brain that's responsible for spatial memory and navigation, were larger in parts with experienced cabbies. Because you have to build all of those mental models, you have to generate new routes every time you have a new passenger. And so they say that that growth, because of the positive correlation with what they have to do, is really meaningful and telling, and it is no different for learning. Durable learning does not come from shortcuts. It comes from certain types of effort. And this is why AI is amazing for education. Because AI can spot patterns in how we all learn. It can spot patterns in how concepts across subjects connect. It can predict if you don't know something and provide you with that material at the right time, it can provide us with timely, targeted interventions and give teachers those insights. It can predict when you're just about to forget something and give you that material at just the right time, it can force you to generate an answer rather than just reveal the answer. And it can provide amazing, structured feedback against expertly designed rubrics from teachers. So AI well-designed can be phenomenal in education. And we've seen it work. Now a lot of people come to me, students and adults, and they say, but why bother? Because we've got GPS, right? We have AI, we can Google the answer to absolutely anything so we don't need to do this anymore. That's not true. If you think about AI, AI is our history predicting our future. It is brilliant at spotting patterns in data. It has been amazing as a partner in remarkable breakthroughs like drug discovery and protein folding, new materials and crystals. But the thing is, none of that happens with AI in isolation. We humans, we frame the questions. We set the goals, we chose the data sets. We decide which discoveries matter. Our knowledge is not just trivia. It is the raw material of thinking and discovery. AI is not there to replace our expertise. It's there to allow our expertise to expand. And if you think about powered flight, penicillin, electricity, AI itself, humans learned. They went through that productive struggle, right? They built domain expertise, and from that they took a leap in their imagination and they created innovations. So for students who want to cheat and want to use AI to do their homework, for us lifelong learners, right, who are reading and reading and reading and reinforcing that illusion of competence, just remember, you do not get the growth unless you go through the struggle. So whether AI is good or bad for education is totally up to you. Are we designing it well and are you using it to complement or to replace human cognition? So the next time you're learning and you want to invest in yourself, educate yourself, you want to grow and maybe take that leap in imagination, just remember, mental effort is not a flaw in the process. It is a critical feature that allows learning to stick, allows us to build expertise and fuel human ingenuity. Thank you so much for listening to me, and good luck with your AI journey. (Applause and cheers)