The clinical teacher · 2026
Synthesising Generative Artificial Intelligence Competency Domains for Medical Education Faculty.
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Meyers Pamela, Miller Cheryl
Аннотация
BACKGROUND: Artificial intelligence (AI), including generative AI (GenAI), is reshaping clinical care and medical education, requiring medical education faculty to develop new competencies. As GenAI becomes integrated into clinical and teaching workflows, faculty must guide learners in AI-enabled environments. Although existing frameworks outline AI competencies for clinicians or learners, few synthesise competencies specifically for faculty roles. This review addresses that gap by synthesising AI and GenAI competency frameworks to identify GenAI domains relevant to medical education faculty.
METHODS: The authors conducted a narrative review of 24 articles published between January 2020 and February 2025 on AI and GenAI competencies for clinicians, educators and learners. Structured database searches identified articles. Existing AI competency frameworks guided comparison of faculty-relevant GenAI competencies. Through iterative synthesis, the authors identified competency domains consistently represented across frameworks and aligned with educator responsibilities.
FINDINGS: Using Russell et al.'s framework as an organising anchor, six competency domains were identified: (1) Foundational AI Knowledge and Literacy; (2) Ethical, Social, and Legal Policies and Implications; (3) AI-Enhanced Clinical Interactions; (4) Practice-Based Learning, Instructional Design, and Pedagogy; (5) Evidence-Based Evaluation and Assessment; and (6) Systems-Level Workflow and Integration. Existing frameworks differed in emphasis across domains, with clinical and systems-level competencies less consistently represented.
IMPLICATIONS: These domains provide a foundational structure to guide faculty development, curricular decision-making and responsible integration of GenAI into medical education. Future work should translate these domains into observable indicators and assessment strategies while examining how faculty competencies support learner development and responsible GenAI use.
METHODS: The authors conducted a narrative review of 24 articles published between January 2020 and February 2025 on AI and GenAI competencies for clinicians, educators and learners. Structured database searches identified articles. Existing AI competency frameworks guided comparison of faculty-relevant GenAI competencies. Through iterative synthesis, the authors identified competency domains consistently represented across frameworks and aligned with educator responsibilities.
FINDINGS: Using Russell et al.'s framework as an organising anchor, six competency domains were identified: (1) Foundational AI Knowledge and Literacy; (2) Ethical, Social, and Legal Policies and Implications; (3) AI-Enhanced Clinical Interactions; (4) Practice-Based Learning, Instructional Design, and Pedagogy; (5) Evidence-Based Evaluation and Assessment; and (6) Systems-Level Workflow and Integration. Existing frameworks differed in emphasis across domains, with clinical and systems-level competencies less consistently represented.
IMPLICATIONS: These domains provide a foundational structure to guide faculty development, curricular decision-making and responsible integration of GenAI into medical education. Future work should translate these domains into observable indicators and assessment strategies while examining how faculty competencies support learner development and responsible GenAI use.