Teaching in the Gap: AI, Bias, and Building Adaptive Learners

AI has changed what students can produce, how they engage with work, and what it means to learn — and schools are responding in real time. Research tells us that AI can cut both ways: it can offload thinking entirely, or, when people genuinely own the work, it can intensify cognitive demand in ways we’re only beginning to understand. How we structure the classroom determines which of those outcomes we get. This session is about finding out where we are in that journey — individually and collectively — and building a clearer picture of how this technology is shaping us, our students, and our practice.
AI can meet any student where they are — in their language, at their level, on their terms. That’s a genuine unlock for engagement. But what a student does with that personalized access is the deeper question. The same tool that opens a topic up can just as easily close the thinking down — and to choose to offload thinking to AI is to choose to say no to a specific kind of learning. The distance between a student who is deeply invested in a problem and a student who has offloaded it entirely can look surprisingly similar from the outside — and navigating that gap is something even experienced professionals struggle to do for themselves. This session explores how we build the structures and strategies that help students, and ourselves, tell the difference.
This session won’t deliver a tidy set of answers — and that’s the point. The landscape is shifting too fast for fixed solutions. What we can do is walk the problem space together, sharpen our questions, and start building the kind of flexibility in ourselves that we’re going to need from our students. The challenge isn’t just supporting student agency — giving students choices within the structures we define. It’s building toward genuine ownership, where students understand what they’re learning, why it matters, and can navigate for themselves when the tools, the options, and the expectations keep changing.