- Stakes: How costly is error in this task?
- Transfer: Does capability need to extend beyond the immediate task?
- Mastery: Is long-term expertise required for sustained performance?
Through structured scenario analysis, live decision mapping, and guided redesign exercises, you will apply the model to decisions affecting AI-assisted content drafting, assessment generation, feedback automation, learner use of AI for synthesis, and performance support design. You will redesign one AI-enabled learning flow to strengthen independent reasoning rather than unintentionally weaken it.
This session is not about resisting AI. It is about designing AI-powered learning that builds durable expertise.
You will learn how to:
- Apply the Stakes-Transfer-Mastery model to evaluate AI use cases in learning design
- Distinguish between automation that accelerates performance and automation that undermines mastery
- Identify risks such as cognitive offloading, automation bias, and fluency illusion in AI-supported learning
- Redesign AI-enabled learning experiences to preserve depth of capability
