This library page brings together practical AI toolkits for teaching, research, and institutional adoption.
What it includes
Useful toolkit formats include prompt packs, assessment checklists, literature review matrices, disclosure templates, research workflow trackers, and adoption planning resources.
How to use it
Use the toolkits as working documents during FDPs, research scholar workshops, classroom planning, policy discussions, and institutional AI readiness exercises.
Quality principle
Every toolkit should make human review, verification, privacy, and disclosure expectations explicit.
How to adapt this material
- Use the material as a lecture note, pre-reading, workshop handout, classroom discussion prompt, or FDP activity.
- Adjust examples for the audience: students need clarity and boundaries, faculty need teaching applications, and researchers need verification and citation discipline.
- Pair the material with a short reflection task so learners explain where AI helped, what they verified, and what remained their own academic judgment.
Quality check
Before using the material in class or training, review examples for accuracy, privacy, academic integrity, disclosure expectations, and fit with institutional policy.