Project · Council initiative

AI Guidelines for Faculty & Learners In progress

Institutional guidance for safe, ethical, and effective AI use in academic medicine — the first standing charge of the DGSOM AI in Medical Education Council, which Dr. Wang chairs.

Scope

The guidelines under development address transparency, privacy, bias mitigation, human oversight, and appropriate use of AI across teaching, learning, and assessment — for faculty and for learners. They are being aligned with UCLA policy, UC-system health AI governance, and national guidance including the AAMC's emerging AI standards, and are designed as a living document updated continuously as technology, national guidance, and legislation evolve.

Status and timeline

Where it stands

In active development through the Council's Guidelines working group. An initial version (v1) is targeted for the end of 2026, with continuous updates thereafter.

Companion effort

A core set of institutional AI competencies for UME, GME, and faculty is being drafted in parallel — informed directly by our framework comparison work.

Governance

The Council advises DGSOM education leadership; guidelines are developed with input from informatics, clinical, and education faculty and will be disseminated through institutional channels alongside training and FAQs.

Why it matters

Guidelines written apart from practice tend to be either toothless or obstructive. Ours are grounded in the lab's operational experience — running an IRB-approved AI tutor trial, operating a production AI learning platform under a strict data-retention rule, and mapping competencies into required curriculum — so the guidance reflects what safe, effective AI use in medical education actually requires.