Predictive AI and Learning Data

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.