The future of learning

Candace Thille is an authority in learning science, educational technology, and AI-enabled learning environments. She is closing the two-way gap between the science of learning research and the hands-on practice of instruction to help students learn better. Timely and targeted feedback with the opportunity to apply that feedback is critical to learning, Thille says, and this is an area where AI supporting humans excels. She imagines a day in the not-too-distant future when human educators and AI-enabled assistants unite to help students learn faster and better than ever before. Learning is not a spectator sport, and AI can help us engage with learners – and educators – in new ways, Thille tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast. Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu . Episode Reference Links: Stanford Profile: Candace Thille Connect With Us: Episode Transcripts >>> The Future of Everything Website Connect with Russ >>> Threads / Bluesky / Mastodon Connect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / Facebook Chapters: (00:00:00) Introduction Russ Altman introduces guest Candace Thille, a professor of education at Stanford University. (00:03:16) Path into Learning Science How Candace became interested in improving how people learn. (00:03:47) The Science of Learning An overview of the field and why it’s still developing. (00:04:42) Training Educators How learning science is applied in teacher education. (00:05:17) The Research to Practice Gap Why insights from classrooms rarely feed back into research. (00:06:43) Technology Supporting Teachers Using AI and other technological tools to enhance teaching. (00:09:00) The Open Learning Initiative (OLI) The origins of one of the first large-scale digital learning systems. (00:11:08) Learning with OLI How feedback and structured practice improved student outcomes. (00:13:14) Building OLI Across Disciplines The collaboration between researchers, instructors, and engineers. (00:14:36) The Accelerated Learning Study Evidence that students can learn faster without sacrificing outcomes. (00:18:02) Learning Science at Amazon Applying learning science research to workplace education. (00:22:29) Research as a Feedback Loop Why teaching practice should continuously inform research. (00:24:49) The Importance of Infrastructure Using captured learning data to improve instruction at scale. (00:25:37) Predictive AI for Learning Science The applications of older AI models in learning science research. (00:28:22) Generative AI as a Learning Interface How generative AI can make education more accessible. (00:31:01) The Myth of Learning Styles The misconception that most people have different learning styles. (00:33:30) Future In a Minute Rapid-fire Q&A: new tools, data infrastructure, and supporting learners. (00:35:24) Conclusion Connect With Us: Episode Transcripts >>> The Future of Everything Website Connect with Russ >>> Threads / Bluesky / Mastodon Connect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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The future of learning is an episode from The Future of Everything presented by Stanford Engineering by Stanford Engineering & Russ Altman.

This episode is 00:36:16 long.

This episode was published on Mar 27, 2026.

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