Who Should Attend
University faculty, learning advisers, and student-success staff.
Learning Objectives
- Design scaffolded AI-supported learning routines that preserve student agency.
- Apply course principles to discipline-specific teaching and student learning scenarios.
- Evaluate risks, equity, evidence quality and responsible AI boundaries.
- Produce an implementable teaching resource with peer feedback.
Curriculum & Instructional Hours
| Module | Key Topics | Hours |
|---|---|---|
| Learning partnership principles | Human-led goals, productive struggle, learner agency | 4 |
| Learning task decomposition | Planning, inquiry prompts, checkpoints | 4 |
| AI for explanation and practice | Concept explanations, practice questions, feedback | 4 |
| Metacognition and reflection | Self-explanation, reflection logs, calibration | 4 |
| Verification and source checking | Cross-checking claims, tracing evidence, correcting errors | 4 |
| Learning partner design lab | Prototype a student learning protocol and evaluate it | 4 |
Teaching & Assessment
Learning Methods
Interactive seminars, case analysis, hands-on design studios, peer review and guided reflection.
Assessment
Participation and applied exercises (25%), design artifacts and peer review (35%), final implementation portfolio (40%); proposed.
Program Outcomes
- AI-supported learning protocol
- Student reflection and verification checklist
- Sample learning activity with feedback checkpoints
Program planning notice: Curriculum, duration, faculty, delivery, visits, and completion arrangements are proposed and subject to final institutional confirmation. University visits, visiting scholar placements, academic credit, and partner-issued credentials are not guaranteed.