Faculty Development — AI Ed Wiki

Faculty development — the processes, programs, and institutional supports that help educators develop the skills and confidence to teach effectively with AI. Faculty development spans individual training, curriculum redesign, and institutional policy change.

Faculty development in the AI era

Readiness frameworks: The EPIQ-AI framework identifies four readiness domains: epistemic, pedagogical, institutional, and quality-and-compliance. Faculty readiness is a sociotechnical alignment problem, not just an individual skills gap.

Adoption and confidence: Teacher AI adoption research identifies concerns, support, confidence, and attitudes as key predictors. Faculty Development programs must address all four.

Curriculum integration: Institutional change frameworks and assessment reform require faculty to redesign courses, not just add AI tools.

Training programs: AI upskilling frameworks and TPACK-based preservice training provide models for structured faculty AI education.

Governance and policy: Institutional AI policy analysis documents the gap between institutional ambitions and faculty support capacity.

Connections

Faculty development connects to Teacher AI Competency (the outcome), Teacher Role (how AI changes instructional work), AI Literacy (faculty must model AI literacy for students), and Educational Policy AI (institutional policies that enable or constrain development).