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

Area of Science:

• Medical Education
• Artificial Intelligence
• Anatomy

Background:

• Artificial Intelligence (AI) is revolutionizing medical education with personalized and immersive learning.
• Anatomy education, crucial for clinical practice, requires specialized pedagogical approaches for AI integration.
• Existing AI frameworks are general and not tailored to the unique demands of anatomy learning.

Purpose of the Study:

• To identify the pedagogical gap in AI integration for anatomy education.
• To propose a novel Anatomy-AI Integration Framework.
• To provide a roadmap for intentional and clinically relevant AI implementation in anatomy.

Main Methods:

• Literature review of AI in medical education and clinical frameworks.
• Analysis of cognitive, psychomotor, and affective demands in anatomy learning.
• Development of the four-domain Anatomy-AI Integration Framework.

Main Results:

• Identified a critical gap in discipline-specific AI pedagogical frameworks for anatomy.
• Proposed the Anatomy-AI Integration Framework with four domains: Theoretical Grounding, Competency Alignment, Embodied Learning, and Ethical Engagement.
• Outlined a validation pathway for transitioning the framework from concept to evidence-based practice.

Conclusions:

• The proposed framework addresses the unique needs of anatomy education for effective AI integration.
• This framework guides the development of pedagogically sound and clinically relevant AI tools for anatomy.
• Further research and validation are needed to establish evidence-based AI practices in anatomy education.