Key points are not available for this paper at this time.
Artificial intelligence (AI) is increasingly implemented in medical education, adding new opportunities to improve learning in complex subjects like anatomy. This study assessed the perceived effectiveness of AI-powered tools in undergraduate anatomy education by evaluating the content validity of AI-generated materials, the student-perceived quality of generated questions, and students' perceptions of AI use. A cross-sectional, multi-phase mixed-methods study was conducted at the College of Medicine and Health Sciences, Sultan Qaboos University, Oman (September to December 2025). Three aims were addressed: (1) expert content validity review of AI-generated heart anatomy materials; (2) students' evaluation of AI-generated MCQs across clarity, difficulty, scientific accuracy, and educational utility; (3) a structured survey examined students' perceptions, usage patterns, and attitudes toward AI tools in anatomy education. Findings were interpreted using three complementary frameworks. These linked the study aims to technical quality, psychometric assessment, educational value, ethical considerations, and human-involved review process. AI-generated anatomy content showed favorable, small-panel-dependent validity (CVR = 0.933; 90% above threshold). Students rated the AI-generated MCQs positively, especially for clarity and scientific accuracy, while difficulty scored lowest. AI was widely used for concept clarification and self-testing. Students found it helpful and time-saving, but reported only moderate confidence in its accuracy, limited AI competence, and concerns about ethical use and long-term retention. When mapped to the three frameworks, the findings indicate favorable expert-rated content validity (Roveta), positive learner responses, and indirect learning gains (Kirkpatrick Levels 1-2). They also highlight the need for a human-in-the-loop workflow (QUEST-AI). AI-powered tools can support anatomy education by improving efficiency, engagement, and self-directed learning. However, they should complement rather than replace traditional teaching. Structured guidance and expert validation of generated content are essential to ensure safe and effective integration into medical curricula.
Almamari et al. (Wed,) studied this question.