Navigating AI with pedagogical purpose: Toward an anatomy-centered framework for AI-enhanced learning

Artificial Intelligence (AI) has emerged as a transformative force in medical education, enabling customized, feedback-rich, and immersive learning experiences. Anatomy education, as a discipline that is simultaneously visual, spatial, and clinically foundational, occupies a distinctive position within this technological landscape. Although current literature addresses AI's potential benefits and ethical concerns in medical and anatomical contexts, a critical gap exists regarding pedagogical frameworks specifically tailored to anatomy education. Most existing AI frameworks have been developed for general educational contexts and have not been articulated for discipline-specific domains such as anatomy. This article identifies and explores this pedagogical gap by proposing an Anatomy-AI Integration Framework that situates AI technologies within the cognitive, psychomotor, and affective demands of anatomy education. Drawing from existing literature, clinical education frameworks, and advances in AI-assisted simulation, we present a roadmap to guide AI integration that is pedagogically intentional, clinically relevant, and grounded in systematic analysis of anatomy's unique learning demands. The Anatomy-AI Integration Framework comprises four domains: Theoretical Grounding, Competency Alignment, Embodied Learning, and Ethical Engagement, with a comprehensive validation pathway to move from conceptual model to evidence-based practice.