Applied insights for using Generative Artificial Intelligence in Faculty Development in Health Professions Education

Generative artificial intelligence (GenAI) has rapidly become accessible to health professions educators and learners, creating both opportunities and new responsibilities for faculty development (FD). GenAI can help draft, adapt, and translate educational materials, and can assist faculty developers as a "co-developer." However, GenAI outputs require human oversight, and FD leaders must actively manage accuracy, privacy/confidentiality, equity/fairness, and sustainability concerns. This article offers a perspective on how GenAI can be leveraged as a co-developer in FD by drawing on emerging literature and discussion points from a workshop at the 8th International Faculty Development Conference in the Health Professions.

We provide eight applied insights, organised across key phases of FD work (planning, content creation, delivery, and evaluation). These insights highlight how faculty developers can: ¹ ground GenAI use in explicit principles and governance; ² use GenAI to synthesise needs assessment data; ³ teach prompt literacy through cognitive apprenticeship using a practical prompt framework (CRAFT); ⁴ co-create multilingual and context-specific resources; ⁵ remix and adapt teaching cases to support interprofessional learning while protecting confidentiality and addressing bias; ⁶ support feedback and mentorship with GenAI (including iterative prompting and retrieval-augmented approaches); ⁷ scaffold personalised continuing professional development plans aligned with self-determination theory; and ⁸ evaluate GenAI use continuously and iteratively. A longitudinal interprofessional vignette is woven across the paper to demonstrate how each insight can be applied in practice.

When used responsibly, GenAI can augment FD and help educators work more efficiently without reducing the central role of human judgment, ethics, and contextual expertise. Faculty developers can increase benefits and reduce harms by making their governance explicit, modelling prompt literacy, and embedding quality assurance/quality improvement cycles.