Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: AI Integration in Undergraduate Medical Education: Qualitative Study of Faculty Perspectives in the United Arab Emirates

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background AI is transforming health care, creating an imperative to integrate AI into medical education. While student perspectives are well-studied, faculty views, particularly in non-Western contexts, remain underexplored. Objective This qualitative study explores the perspectives of 10 medical faculty members from 2 institutions in the United Arab Emirates on integrating AI into undergraduate medical education. Methods This multi-institutional qualitative study used purposive and reflexive sampling to recruit faculty from both public and private medical universities in the United Arab Emirates. Semistructured interviews were conducted with 10 faculty members involved in curriculum design, teaching, or assessment. Data collection followed COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines. Data were analyzed using a mixed inductive-deductive approach guided by the thematic analysis framework of Braun and Clarke. Findings were interpreted using the FACETS (Form, AI Use Case, Context, Education, Technology, and SAMR: Substitution, Augmentation, Modification, Redefinition) framework, which supported a structured examination of AI integration across different dimensions of teaching and learning. Results Faculty primarily used generative AI tools, such as ChatGPT, for content creation, assessment development, and teaching support, reflecting a preference for accessible and general-purpose technologies. AI was mainly used to enhance teaching efficiency and support student learning, including personalized study planning and practice activities. Its application extended across preclinical and clinical contexts, with strong emphasis on adapting content to local cultural and ethical norms. While AI was perceived to improve efficiency and alignment between teaching and assessment, concerns were raised regarding equity, overreliance, and variability in student use. Overall, adoption remained focused on enhancing existing practices, with limited transformative use but recognition of future potential for more advanced applications. Conclusions The UAE medical faculty demonstrate cautious optimism toward AI integration, recognizing its potential to enhance educational efficiency and personalization while emphasizing the critical importance of cultural contextualization. Current implementation remains at early adoption stages, focused on enhancement rather than transformation. Successful integration requires faculty development, context-sensitive policies, and equitable implementation strategies that address both technological and sociocultural dimensions of AI adoption in medical education.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Azhar T Rahma; Uffaira Hafeez; Munawar Farooq. AI Integration in Undergraduate Medical Education: Qualitative Study of Faculty Perspectives in the United Arab Emirates. JMIR Medical Education (2026-08-13). https://doi.org/10.2196/90192

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.