Who Should Attend
Faculty, academic leaders, teaching centers and quality assurance teams.
Learning Objectives
- Explain core AI capabilities, limitations and risks in academic settings.
- Evaluate educational use cases using evidence and human oversight.
- Develop responsible institutional AI guidance.
- Design a feasible department-level pilot and evaluation plan.
Curriculum & Instructional Hours
| Module | Key Topics | Hours |
|---|---|---|
| AI Foundations for Universities | Generative AI capabilities, model limitations, verification | 5 |
| Teaching and Learning Applications | Feedback, tutoring, accessibility, instructor oversight | 5 |
| Academic Integrity and Assessment | Disclosure, authentic assessment, student work | 5 |
| Privacy, Equity and Governance | Data handling, bias, institutional policies | 5 |
| Implementation and Evaluation | Pilot selection, risk registers, outcome measures | 5 |
| Institutional AI Capstone | AI use-case review and pilot proposal | 5 |
Teaching & Assessment
Learning Methods
Interactive workshops, guided labs, peer review, case discussions and applied capstone projects.
Assessment
Workshop engagement (25%), applied exercises and draft artifacts (35%), final capstone project (40%); proposed.
Program Outcomes
- AI opportunities and risk assessment
- Draft institutional AI use guidelines
- Pilot evaluation framework
- Department-level implementation proposal
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.