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If this episode makes you think, please let us know in the comments and support us by subscribing and leaving a review. Thank you. Today we are exploring a strike, an article built around a conversation with Judit Polgar, the greatest female chess player in history, and what she thinks schools need to understand about AI because chess lived through something similar decades ago. The article argues that chess was reshaped by artificial intelligence long before classrooms were, and Polgar's reflections now offer educators a powerful warning about trust, passive knowledge, intuition, and the danger of confusing fast answers with real understanding. What makes this so interesting is that Polgar is not speaking as a classroom theorist. She is speaking as someone who lived through a professional world being changed by machine intelligence. According to the article, she remembers the fear, the confusion, and the loss of trust that came when chess engines moved from curiosities to dominant tools. And when she looks at schools now, she says she sees the same thing in teachers, the same insecurity, the same question underneath it all, Am I still needed? That is such an important place to begin, because AI in education is not just a technical change, it is an emotional one. Teachers are not simply being asked to learn another tool. They are being asked to rethink what expertise means when a machine can instantly generate explanations, quizzes, feedback summaries, and polished answers. That is deeply unsettling. And according to the article, Polgar describes it as confusion, almost like an earthquake, where you do not know what is going to remain and what is going to disappear. Now here is where the article becomes really useful for schools. Polgar tells a story from her own chess career. She she had prepared for hours with a chess engine. She entered a game feeling confident. Then she reached a moment where her instinct told her to take a draw. But the computer analysis she had trusted suggested she was winning. So she trusted the machine instead of her own judgment, pushed on, and lost the game. That story is not really about chess. It is about a question educators and students are now facing every day. When the machine sounds confident and the human feels uncertain, the who do you trust? Because that moment is everywhere. Now the AI gives you an answer that sounds perfect. The feedback looks polished, the summary seems complete, the essay reads well, and yet something in you says, I'm not sure this is right. Or at least not right enough. That tension between machine fluency and human judgment is going to define a huge part of education in the AI era. And then Polgar says something I think schools should take very seriously. According to the article, even if a chatbot gives you the right answer, the Problem is that it is too perfect. She calls it passive knowledge. That phrase is brilliant passive knowledge because it names a risk that is much bigger than obvious cheating. The real problem is not just the student who copies and gets caught. It is the student who copies, gets a decent grade, and and builds nothing they can actually use later. They have seen the answer, maybe even repeated it, but they have not built the understanding underneath it. The article gives a chess example. Young players use engines to memorize opening moves. They know the first 10 or 12 moves. Then the opponent does something unexpected and they are stranded. They have no plan, no deep understanding, no ability to adapt. And Polgar's line is devastating. It is worse than nothing because you think you know it, but you do not know it. That is the trap schools should worry about most. Because once a student thinks they know something when really they have only borrowed it, the weakness becomes much harder to spot. The output looks fine. The confidence may even look real. But the capability is hollow. And that is why the article's core message lands so hard for education. The finished answer is not the same as learning. The polished product is not the same as understanding. Polgar puts this in a wonderfully simple way. According to the piece, she tells her students that the good move is only 10%. The other 90% is understanding why it is a good move. 10% and 90%. That is such a useful ratio for schools. The final answer might be the visible bit, but the real learning is often in the invisible bit. The reasoning, the struggle, the sequencing, the judgment, the ability to transfer it when the context changes. If schools only reward the 10%, AI will make that weakness very obvious very quickly. The article then turns to intuition, and I think this may be one of the most important ideas in the whole piece. Polgar says intuition is not magic. It comes from experience. It comes from time spent doing, practicing, struggling, and slowly building patterns you can later recognize and use. Her concern is that young people may not build that intuition if they do not spend enough time actually doing the work themselves. That matters enormously for education. A chatbot can give an answer in seconds, but it cannot give a learner the hours of wrestling that would have built the instinct for the next problem. It can shortcut the path to output. It cannot always shortcut the path to capability. And this is where I think educators need to be very careful. We often talk about removing friction, as though friction is always bad. But in learning, some friction is the point. Not pointless frustration, not badly designed difficulty, but productive struggle, the kind that helps a learner build memory, pattern recognition, Resilience and judgment. If AI removes every hard edge, then it may also remove some of the conditions under which understanding becomes durable. The article also touches on failure, and this section is really strong. Polgar argues that failure is not a glitch in learning, it is part of it. She worries that students who spend too much time with agreeable AI systems may become less used to being challenged, being wrong, or sitting with discomfort. And she says schools may need to teach children how to benefit from the difficult and the bad. Even suggesting the idea of a deliberate journey through mistakes. I love that, because it gets to the heart of something schools cannot afford to forget. Learning is not just getting to the right answer. It is becoming someone who can recover from being wrong, someone who can think again, someone who can hold uncertainty without collapsing into helplessness. AI can help with many things, but it cannot build that inner strength for the learner that still has to be lived through. And then the article moves into something even broader. Polgar says that when knowledge can be reached instantly on a phone, the most important things in education are not lectures and information delivery. They are human connection, critical thinking, and the rebuilding of thought. That lands hard because it pushes us to ask what the real value of school is. Now, if information is abundant, then school cannot define itself by access to information alone. Its value has to be in the thinking around it. The challenge, the dialogue, the interpretation, the feedback, the context. The live encounter with another human being who notices questions, supports and stretches you. That is why I do not think the answer to AI is more surveillance or a desperate attempt to preserve the old system unchanged. The better response is to redesign learning so that human thinking stays visible. More explanation, more live performance, more reflection, more oral defense, more process, not just product. In other words, design learning that cannot be faked because it demands depth, care and imagination. And I think there is a comfort in this for teachers, because the article is not really saying teachers matter less. It is saying the opposite. If passive knowledge is the danger, then the teacher's role becomes even more important. Not just delivering information, but creating the conditions where understanding is built. Noticing when a student has borrowed fluency without owning meaning. Asking the question that unsettles false confidence. Protecting the struggle that turns into judgment. Machines can compute. They cannot wonder, they cannot care. The final part of the article really stays with me. Polgar says she is not anti AI. She uses chess engines daily. She knows the technology is here. What she calls for instead is bravery. Educators willing to move forward without pretending they already have the full answer. She uses this wonderful image of being in a jungle with a machete, trying one path, then another, cutting forward without a map. That feels right to me. There is no neat answer here. The question is not whether AI should exist in schools. That ship has sailed. The question is whether schools are protecting the conditions under which children become people who can think, judge and recover from being wrong. According to this article, Chess learned that lesson the hard way. Schools still have time to learn it more intentionally, but not much time. That's all for today. Thanks for listening.