Who Should Attend
Faculty, learning-support professionals, and academic advisers.
Learning Objectives
- Build practical student AI literacy across capability, limits, verification and responsible use.
- 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 |
|---|---|---|
| AI foundations for learners | Capabilities, limitations, model errors | 4 |
| Question framing and prompting | Clear goals, constraints, iterative prompts | 4 |
| Evaluating AI outputs | Evidence checks, bias, uncertainty, citations | 4 |
| Data and privacy literacy | Sensitive data, account settings, institutional rules | 4 |
| AI-supported study practices | Revision, tutoring, accessibility, independent practice | 4 |
| AI literacy curriculum lab | Design literacy activities and evaluation rubric | 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
- Student AI literacy competency map
- Practical evaluation rubric
- AI literacy mini-lesson and student guide
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.