The future of learning
The Future of Everything
Research as a Continuous Feedback Loop
She advocates treating every teaching act as data and making practitioners collaborative researchers with proper infrastructure.
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.
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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
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