A different way to see learning.

Practice scenarios vary what the learner says. They rarely vary who the other person is. This talk explores designing the counterpart with a hidden state the learner has to read, and what that changes about a simulation’s learning possibilities.

Best for: Leadership development, learning design conferences, and keynote audiences.

Take away: A way to examine counterpart design, readable cues, and the decisions a practice scenario asks learners to make.

AI can raise performance in a session without building skill that lasts. This talk examines a practical evaluation rule: without the support, after a delay, in a case the learner hasn’t seen.

Best for: L&D and AI enablement conferences, evaluation tracks, and learning teams.

Take away: Questions for planning unsupported, delayed, and unfamiliar-case assessments alongside in-session measures.

What does a learner need to understand before an AI-mediated scene, during it, and after? This talk explores disclosure as an experience-design decision, with attention to learner expectations, boundaries, and transparency.

Best for: Ethics panels, university courses, HR, compliance, and learning-design teams.

Take away: A before, during, and after lens for examining how an AI experience explains itself to learners.