AI in the Classroom: Why AI-Powered Personalized Learning Might Not Deliver on its Promise
AI is everywhere—and it’s evolving rapidly. From predictive algorithms to large language models like ChatGPT, artificial intelligence is reshaping how we work, communicate, and learn. As schools explore AI in the classroom, educators and researchers are asking: Does AI really understand us? Or are we projecting human-like thinking onto systems that are fundamentally different? The stakes are high: while AI promises personalized learning at scale, it may be missing key ingredients of how humans truly think and learn.
So what does it mean to say that “AI doesn’t think like us”? And if that’s true, what are the implications for education?
In this episode of Class Disrupted, part of The Future of Education podcast, co-hosts Michael Horn and Diane Tavenner welcome Benjamin Riley, founder of Cognitive Resonance. Riley brings a thoughtful, research-grounded skepticism to the hype around AI. He’s deeply curious—but not convinced—that current tools can meet the cognitive and cultural demands of education.
The key topics of conversation…
- Why AI lacks a “theory of mind”—a crucial human capacity—and what that means for student learning.
- Why the dream of personalized AI in the classroom may fall short, despite technological advances..
- How studying AI can still teach us about human cognition, even if the machines don’t “think” like we do.
Benjamin Riley is the founder of Cognitive Resonance, a consultancy focused on applying cognitive science to improve decision-making, particularly in education and AI. He previously founded and led Deans for Impact, a national nonprofit transforming teacher preparation to improve student learning outcomes. With a background in law and public policy, Riley has held roles ranging from Deputy Attorney General in California to policy leader at NewSchools Venture Fund and a public policy fellow in New Zealand’s Ministry of Education.
