JMIR Publications

ABSTRACT

Background:

Background Artificial Intelligence (AI) is transforming healthcare, 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:

Objective This study examines medical faculty perspectives on AI integration in undergraduate medical education within the United Arab Emirates (UAE), focusing on pedagogical applications, implementation challenges, and context-specific considerations.

Methods:

Methods This multi-institutional qualitative study employed purposive and reflexive sampling to recruit faculty from both public and private medical universities in the UAE. Semi-structured interviews were conducted with ten faculty members involved in curriculum design, teaching, or assessment. Data collection followed COREQ guidelines, with analysis using a mixed inductive-deductive approach guided by Braun and Clarke's thematic analysis framework. Findings were organized and interpreted through the FACETS (Form, AI Use Case, Context, Education, Technology, SAMR) framework and SAMR (Substitution, Augmentation, Modification, Redefinition) model.

Results:

Results Analysis revealed six key themes aligned with the FACETS framework. Faculty primarily used Natural Language Processing tools (Form) for assessment generation and personalized learning (AI Use Case). Integration spanned preclinical and clinical settings, with strong emphasis on cultural and ethical localization for the UAE context (Context). AI enhanced assessment alignment and teaching efficiency (Education) through accessible, general-purpose tools (Technology). SAMR analysis indicated current use predominantly at Substitution and Augmentation levels, with emerging recognition of Redefinition potential. Faculty identified urgent needs for professional development and concerns about equity and overreliance.

Conclusions:

Conclusion 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 socio-cultural dimensions of AI adoption in medical education.