Why models must ask more questions

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Rene Kizilcec is an Associate Professor at Cornell University, where he directs the Cornell Future of Learning Lab and leads the National Tutoring Observatory. His research focuses on learning science, AI in education, and the behavioral and computational factors that shape student success. His work has appeared in Science, PNAS, and other top journals.

💡 5 Things You’ll Learn in This Episode

1. How the National Tutoring Observatory is building the largest dataset of real tutoring interactions.
2. What specific “tutoring moves” actually drive learning outcomes.
3. How AI models can be trained to use stronger pedagogical strategies.
4. Why education needs real benchmarks for measuring teaching quality in AI.
5. How simulation-based learning is transforming medical and language training at Cornell.

✨ Episode Highlights
[00:00:00] Rene on aligning AI study tools with real pedagogy.
[00:02:33] Lessons from early MOOC research on equity and completion.
[00:05:58] How motivation and belonging interventions improved outcomes.
[00:10:57] The mission and structure of the National Tutoring Observatory.
[00:13:01] Why tutoring works—and why we haven’t known which moves matter.
[00:18:28] Tools for identifying tutoring moves across huge datasets.
[00:20:00] Using tutoring data to influence hyperscalers and AI product design.
[00:22:37] The need for robust benchmarks for AI tutoring quality.
[00:27:42] Why great tutors ask questions instead of giving answers.
[00:31:03] How tutoring data can improve AI models while preserving privacy.
[00:34:34] How tutoring providers can join the NTO consortium.
[00:37:16] Simulation-based learning with MedSimAI and Chitter Chatter.
[00:41:34] How AI-assisted development accelerates EdTech innovation.

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