Design Sprint Recap: Designing AI-Resilient Learning Experiences

In this special summer episode of the AI for Educators: Design Lab podcast, Jennifer Maddrell reflects on a four-day Design Sprint with educators from K–12, higher education, and professional learning. She explores the idea that AI may be exposing “pedagogical deferred maintenance” rather than creating entirely new problems in teaching and learning.

Framing AI integration as an ill-structured design challenge, Jennifer examines how learning experiences can become more AI-resilient by reconsidering the alignment among four connected elements: learning goals, learning tasks, the visibility of the learning process, and the evidence used to measure learning.

Using her own graduate annotated bibliography assignment, which Claude completed convincingly in less than five minutes, she illustrates how product-focused tasks and invisible learning processes can create AI vulnerability. She also explains how the Design Sprint used the Double Diamond model, an AI-vulnerability test, and research-informed design responses to help educators diagnose before they solve.

00:00 Summer Edition Welcome

01:00 Design Sprint Recap

01:42 Pedagogical Deferred Maintenance

04:42 Four Connected Elements

07:17 Annotated Bibliography Example

11:33 The Double Diamond Design Process

12:37 Diagnose Before You Solve

15:01 Design Responses Across Four Elements

15:15 Learning Goals in the AI Era

17:02 Learning Tasks That Preserve the Thinking

17:59 Making the Learning Process Visible

19:24 Rethinking the Evidence That Carries the Grade

20:40 Key Takeaways and Next Steps

23:01 On-Demand Design Sprint

23:58 Closing