Actionable companion · School leadership and instructional improvement

From leadership research to district action: Role-Differentiated AI Competencies and Curriculum Implications for Health Professions Education: Qualitative Study

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: research-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Abstract Background As AI fundamentally transforms the healthcare landscape, medical education curricula have struggled to keep pace with these technological shifts. While current research has established a foundation for general AI literacy, less attention has been given to the role-specific competencies required for the diverse functions that healthcare professionals perform in an AI-integrated environment. Although existing tiered and domain-based competency models have clarified general AI competency requirements, they offer limited guidance on how such competencies should be differentiated according to the roles healthcare professionals perform in practice. Objective This study aimed to explore how AI-driven changes in healthcare shape competency requirements across professional roles and to develop differentiated competency frameworks and curriculum implications for 3 distinct roles: users, developers, and leaders. Methods Using a qualitative research design, we conducted in-depth interviews with 13 subject matter experts across academic medicine and dentistry, clinical practice, and the healthcare AI industry, who were recruited through 4 independent channels. Data were analyzed using reflexive thematic analysis with inductive coding organized around 3 research questions and published competency frameworks serving as sensitizing concepts. Trustworthiness was supported through investigator triangulation, member checking, an audit trail, and attention to researcher reflexivity and positionality. Results We identified a role-differentiated competency framework with a layered structure as follows: (1) Users require machine learning and data literacy to understand where algorithms fail and scrutinize data origins and biases; the ability to select best-fit AI solutions and set appropriate thresholds for human-machine delegation; and a high degree of AI-related professionalism to work responsibly with AI by recognizing its limits, critically appraising outputs, maintaining clinical accountability, preserving patient-centered care, and updating knowledge continuously; (2) Developers add operational competencies, including validating systems across technical performance, clinical relevance, and regulatory compliance; bridging the language gap between clinical needs and technical refinements; and fostering a challenging, problem-solving mindset to address real-world clinical bottlenecks; and (3) Leaders focus on system-level competencies, including macro-level strategic planning, governing medical AI across its full lifecycle—selection, validation, deployment, and monitoring—through policy “roads” and governance frameworks, and optimizing systemic resource utilization while orchestrating trust-based collaboration that safeguards care quality. Rather than a strictly cumulative hierarchy, the roles represent analytic distinctions with shared foundational competencies. This framework translates these competencies into corresponding curriculum implications across the health professions education continuum. Conclusions This study identifies role-differentiated AI competencies and proposes a corresponding curriculum scaffold for healthcare education. As a preliminary, hypothesis-generating starting point requiring multistakeholder validation, the proposed framework can help prepare the future healthcare workforce to utilize AI responsibly and to govern and lead the next generation of digital health innovation.

A 90-day district inquiry

1. Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
2. Days 16–30 · Design. Choose one school improvement question with teachers. Protect a short shared planning cycle, agree an instructional change and use a common rubric for examining student work. Agree participation, privacy protections, baseline and stopping rules before starting.
3. Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
4. Days 61–90 · Decide. Review implementation fidelity, teacher time and independently scored student work against a recorded baseline. Examine access differences and competing explanations before attributing changes to leadership. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Yuyi Park; Yejin Han; Hyeongjo Kim; Solmoe Ahn; Frederick William Kron; Jihyun Lee. Role-Differentiated AI Competencies and Curriculum Implications for Health Professions Education: Qualitative Study. JMIR Medical Education (2026-09-08). https://doi.org/10.2196/97608

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.