AI That Makes Students Think: Retrieval Practice, Feedback, and Smarter Classroom Implementation

Host Kane interviews Kevin, a Philadelphia educator with 12+ years’ experience and a PhD in educational psychology focused on retrieval practice. They discuss how retrieval improves learning, why feedback timing and quality matter, and how AI can scale personalized practice through tools like Flint and other Socratic dialogue and writing-tutor products. Kevin explains using ChatGPT for self-testing, argues AI can provide more consistent feedback than student notes, and emphasizes keeping the “ball” with students by avoiding AI synthesis and maximizing reps. They also cover uneven school readiness for technology, the need for faculty training, tensions between regulation and experimentation, and the importance of implementation alongside product design, including a checklist of principles such as making learning hard, focusing on weak skills, and ensuring students get the most practice.

Screen image is in the FaceBook group (link below)

00:00 AI Without Cheating

01:02 Meet Kevin

01:28 Retrieval Practice Basics

03:28 Nuances That Matter

04:43 ChatGPT For Retrieval

06:35 Scaling Feedback With Chatbots

09:34 Tool Design Principles

11:40 School Readiness For AI

14:30 Training And Policy Tensions

17:27 Assessment Pressures And AI

20:44 Teacher Built AI Resources

22:35 Checklist For Better Learning

26:24 Design Meets Implementation

27:38 Products And Wrap Up

28:35 Off The Record Farewell

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