Who Should Attend
Faculty, course coordinators, academic integrity teams.
Learning Objectives
- Distinguish legitimate support from prohibited substitution across learning tasks.
- 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 |
|---|---|---|
| Defining academic contribution | Learning outcomes, authorship, independent work | 4 |
| Task-specific AI boundaries | Brainstorming, editing, coding, analysis and writing | 4 |
| Disclosure and attribution | AI-use statements, provenance, transparency | 4 |
| Assignment risk analysis | Vulnerable tasks, process evidence, equity | 4 |
| Policy and student communication | Clear rules, examples, appeals and fairness | 4 |
| Boundary-setting workshop | Draft a course AI-use matrix and policy | 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
- Assignment-level AI use permission matrix
- Student-facing AI disclosure guidance
- Course policy and implementation checklist
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.