The principles behind AI reinforcement learning offer fascinating insights for human education, showing how both machines and people benefit from balanced exploration, timely feedback, and positive reinforcement. This article bridges the gap between clicker training, AI learning methods, and classroom teaching, suggesting that understanding these shared principles can lead to better educational approaches.

In today's rapidly changing job market, adaptability is the most crucial career skill. AI can help develop wisdom skills through concept stretching, judgment playgrounds, and interdisciplinary analogies. By focusing on broadening perspectives and creating abstract connections, professionals can better prepare for novel situations and career uncertainty.

Our education system is misaligned with the real world, teaching outdated skills and failing to prepare students for modern challenges. This misalignment leads to disengagement and poor retention of knowledge. We need to rethink curriculum around relevant, multidisciplinary approaches that focus on transferable skills and future-oriented tasks.

Common misconceptions about human attention, such as the flashlight and whiteboard analogies, are shaping education in counterproductive ways. Research shows attention functions more like an adaptive filter, is only weakly under our control, and operates dynamically. Understanding these realities can lead to more effective educational practices aligned with how our brains actually work.

AI has accurately summarized my book "Wisdom Factories" 18 months after its publication, highlighting its enduring relevance in the rapidly evolving AI landscape. The book's core ideas about transforming education for an AI-dominated world remain valid, emphasizing the shift from expertise-focused learning to wisdom-oriented skills. This summary serves as a foundation for discussing how these ideas have matured and evolved since the book's release.

Explore how AI revolutionizes education beyond the classroom, optimizing curriculum design and school administration. Learn about AI's potential to solve complex educational challenges, improve resource allocation, and create more effective learning experiences. Discover the future of AI-assisted educational planning and its impact on student outcomes.

Explore how AI is reshaping math education needs, moving beyond 'moon launch math' to focus on problem-solving, statistical thinking, and tech-assisted calculations. Learn why traditional curricula fall short in preparing students for a world where AI handles computations, and discover the push for a more relevant, conceptual approach to mathematics in schools.

Uncover AI's 'mind' through associative networks and neural visualizations. Learn how knowledge graphs and attention heatmaps reveal AI thinking processes. Explore connections between AI cognition and human learning across diverse subjects.

Explore optimization terrains and confusion matrices in AI education. Visualize how AI navigates solution landscapes and evaluates performance. Gain insights into machine learning processes and error analysis applicable across various fields.

Discover how to wire AI education into curricula by starting small: from hand-wiring neural networks to simulating swarm behavior in classrooms. Explore practical, subject-specific activities that teach pattern recognition, self-organization, and optimization across K-12, fostering AI intuition beyond just prompting skills.

Discover how to wire AI education into curricula by starting small: from hand-wiring neural networks to simulating swarm behavior in classrooms. Explore practical, subject-specific activities that teach pattern recognition, self-organization, and optimization across K-12, fostering AI intuition beyond just prompting skills.

Discover how to wire AI education into curricula by starting small: from hand-wiring neural networks to simulating swarm behavior in classrooms. Explore practical, subject-specific activities that teach pattern recognition, self-organization, and optimization across K-12, fostering AI intuition beyond just prompting skills.

Sweet GrAIpes newsletter explores AI's role in education, challenging the 'sour grapes' mentality towards seemingly unattainable educational goals. It discusses how AI could enable individualized learning, critical thinking, creativity, and emotional resilience. The newsletter covers cognition, curriculum, pedagogy, and leadership across secondary, higher, and corporate education sectors.

One big aspect of getting better at thinking about the whole is that it is driven by a part of the brain that doesn't like focused attention. Mind wandering has value too, especially for durable skills like creativity. And today's distracted, over-scheduled youth don't get to practice it. Mind wandering isn’t a human defect!

The bottom-up way of educating isn't the only way we learn. We also learn by explicitly focusing on the whole. Wholistic thinking is its own thing, separate from the detailed analysis (though of course coupled as we bounce back and forth between those thinking modes). In my article this week I describe the importance of teaching the whole, not just the parts.

I've been telling educators that I think there are a set of thinking and learning fundamentals that are especially important in the AI era, but have been relatively ignored in curricula (at least explicitly). In my view, these are the concepts that transcent today's AI products or classroom environments and will be forever fundamental. These are the deep learning aspects of AI literacy (in the educational sense of deep learning, not the AI method). My article this week describes aspects of one of those fundamentals: categorization

The main difficulty teaching durable skills like critical thinking and creativity is that it inherently needs concepts transferable to many domains, but curriculum is typically carved by domain.

That means teachers need to do their part to connect concepts to other fields. In this article I call that concept stretching.

And as you might expect, AI is super helpful.

There is always a gulf between technology providers and users, but the one between schools and edtech is mammoth and frequently peppered with disrespect. Part of the reason for this is that there are key matchmaking roles that are missing in the education sector, as described in my article this week.

Of course we want evidence-based learning, right? Except when educators insist on evidence before trying something different and the evidence is absent, then they're locking themselves into the current paradigm. Absence of evidence shouldn't be treated the same as evidence of negative outcomes.

In my new article I describe the main problems with evidence-based learning (while all the while acknowledging that I'm a scientist who loves evidence!)

1. Most educational evidence is weaker than advertised.

2. Evidence on learning durable skills is largely absent.

3. Insistence on evidence-based learning impedes progress on new skill needs.

I think schools and teachers tend to have precisely the wrong reaction to disruption and disengagement. They clamp down on student agency in the hope that a dutiful classroom learns more. The correct reaction in my view is just the opposite. Disruption is a sign that students don't have enough agency in their learning path. Insistence on dutiful students is inconsistent with the desire to have creative, critically thinking students.