Integrating Generative Artificial Intelligence (AI) in Medical Education: A Framework for Preserving Clinical Reasoning

Generative artificial intelligence (AI) is increasingly present in medical education, yet its indiscriminate use risks impairing the acquisition of foundational clinical competencies, including clinical reasoning, hypothesis generation, and patient-centered communication, through processes of never-skilling, mis-skilling, and deskilling. This paper presents M3RGE-AI (Responsible, Reliable, and Reflexive use of Generative AI in Medical Education), a conceptual framework for the purposeful integration of AI as a cognitive scaffold in medical training. Drawing on established learning theories, zone of proximal development, deliberate practice, and peer learning, the framework assigns progressively expanding AI functions across training stages, prioritizes Socratic over directive interactions, requires transparent and verifiable sourcing of AI-generated content, and incorporates peer moderation and AI-off assessment checkpoints to mitigate over-reliance. The framework is operationalized through alternating AI-on and AI-off cycles, governance processes, and educator training protocols. Applied within these constraints, AI can shorten feedback loops and broaden clinical exposure while preserving independent reasoning and authentic patient communication. M3RGE-AI offers a theoretically grounded and institutionally implementable model for integrating generative AI into medical curricula without sacrificing the essential human competencies that underpin safe clinical practice.