Enabling Rigorous, Equitable, and Evidence-Based Artificial Intelligence in Health Professions Education

Artificial intelligence is already in the hands of health professions learners, educators, simulationists, program leaders, and educational administrators. Health professions education has moved through an initial phase of rapid AI adoption characterized by enthusiasm, experimentation, and proliferation of small-scale demonstrations. The field now needs a different kind of literature: one that supports educators and program leaders in making evidence-informed decisions about when AI improves learning, when it does not, and under what conditions improvements are likely to generalize. Three gaps motivate this Research Topic: published work frequently reports proof-of-concept or learner-satisfaction outcomes without sufficient design rigor for causal claims; AI competencies are asserted but rarely operationalized in assessable ways; and the disclosure of AI use—by learners, educators, and researchers—remains inconsistent, limiting the interpretability of the evidence base.This Research Topic seeks evidence-based scholarship that advances the effective, transparent, and responsible use of AI in health professions education across the educational continuum and across professions. It aims to stimulate rigorous inquiry into AI-related competencies, AI-enhanced educational design, evidence-based practice in the AI era, implementation science, and disclosure norms—producing a body of work genuinely useful to program leaders, accreditors, and educators making real decisions about AI integration.Submissions are welcome from across the full range of health professions and from diverse global contexts. Evidence-focused empirical studies are a priority; conceptual contributions that contribute to equitable, evidenced-based integration of artificial intelligence will be included, particularly those that provide operational definitions, assessment frameworks, or reporting standards the field can apply. Possible areas for articles include:• Competency definition, assessment, and curricular integration for AI-enabled health professions educational practices• Design and effectiveness of AI-enhanced education: comparative studies, simulation, assessment, scaffolding, and feedback• Evidence-based, equitable educational efforts: appraising the AI-in-education literature, translating evidence into student and program decisions, and adapting reporting standards• What works, for whom, and under what conditions: implementation studies reporting context, fidelity, direct and indirect costs, risks, and benefits, scalability, methodological considerations, and implications of AI-assisted learning. Studies with negative and null results are welcome.• AI-assisted educational activities linked to quality improvement and patient or clinician or system-level outcomes.• Disclosure of AI use in educational practice and research: policies, frameworks, and evaluations across coursework, curriculum development, and scholarly publicationsArticle types accepted include Original Research, Systematic and Scoping Reviews, Hypothesis and Theory, Curriculum, Instruction, and Pedagogy, Methods, Brief Research Report, Community Case Study, Study Protocol, and Perspective. Topic editors: Roger Edwards, Cynthia Mosher, Sateesh Babu Arja Source: https://www.frontiersin.org/research-topics/83064/enabling-rigorous-equitable-and-evidence-based-artificial-intelligence-in-health-professions-education