JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is transforming medical and health professions education, yet its integration within as anatomy remains uneven and often ad hoc. Anatomy’s heavy spatial demands, reliance on cadaveric and imaging resources, and central role in preclinical curricula make it a prime site for AI adoption. Understanding how anatomy educators in the United Arab Emirates (UAE) perceive AI’s opportunities and threats is therefore essential to guide responsible, discipline-sensitive implementation.

Objective:

This study examined UAE anatomy educators’ use of AI, attitudes, perceived barriers and enablers, and strategic perspectives on AI integration in anatomy education, using a design informed by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2).

Methods:

A cross-sectional survey of anatomy faculty at UAE medical and health sciences colleges used 5-point Likert items to assess educational technology proficiency, AI usage patterns, AI attitudes, perceived barriers and facilitators, and professional-development needs. Quantitative data were summarized descriptively and explored with non-parametric tests. Open-ended questions captured Strengths, Weaknesses, Opportunities, and Threats (SWOT) related to AI use; responses underwent reflexive thematic analysis and were organized within the SWOT framework, then interpreted through UTAUT2 constructs (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit, and behavioral intention). Quantitative and qualitative strands were integrated at interpretation through triangulation

Results:

Thirty anatomy faculty participated. Self-rated educational technology proficiency was high (M=3.73±1.01/5), and overall attitudes toward AI in anatomy education were positive (M=4.23±0.73), with strong interest in AI-focused professional development (M=4.50±0.73). Most respondents reported using generative AI (GenAI) tools, predominantly ChatGPT, for content creation, quiz and exam item generation, summarizing complex material, and, to a lesser extent, visualization and workflow streamlining. Capacity-related barriers predominated: time and workload pressures (M=3.27±1.17) and training gaps (M=3.13±1.22) were rated as moderate obstacles, whereas budget/resource limitations (M=2.63±1.19) and academic integrity concerns (M=2.80±1.10) were minor. Student interest (M=4.23±0.86) and institutional encouragement (M=4.00±1.14) emerged as strong facilitators, with no statistically detectable differences by academic rank, age, or years of experience in this small, underpowered sample. Qualitatively, themes highlighted strong institutional support and digital readiness as strengths; training needs, workload, and policy gaps as weaknesses; visualization, personalization, and efficiency as opportunities; and overreliance, ethical risks, and erosion of hands-on anatomy pedagogy as threats. UTAUT2 interpretation indicated high performance expectancy and social influence (student and institutional support), but reduced effort expectancy and facilitating conditions due to time, training, and governance constraints, collectively tempering behavioral intention.

Conclusions:

In this exploratory sample, UAE anatomy educators were broadly receptive to GenAI and already experimenting, valuing benefits for 3D visualization, adaptive practice, and feedback. However, workload, limited training, and unclear governance (disclosure, assessment integrity, cadaveric/patient images) constrain uptake, underscoring the need for protected time, workflow-aligned training, and discipline-specific policies to enable sustainable, ethical integration.