Integrating Artificial Intelligence and Socratic Inquiry in Medical Education A Critical Framework for Clinical Reasoning Development
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Background : The integration of artificial intelligence (AI) in medical education represents a paradigm shift in pedagogical delivery, particularly in addressing the persistent gap between basic science knowledge and clinical application. The Socratic method, while theoretically ideal for developing critical thinking, faces significant scalability and implementation challenges in modern medical curricula. Objectives : This comprehensive review examines the potential of AI-powered systems to emulate Socratic dialogue in basic medical science education, analyzing current applications, comparative effectiveness, inherent limitations, and ethical considerations. Methods : We conducted a critical synthesis of peer-reviewed literature (2019-2025) on AI applications in medical education, Socratic pedagogy, and educational technology. Data sources included PubMed, ERIC, Web of Science, and educational technology databases, yielding 67 relevant studies. Results:: AI-driven Socratic tutoring systems demonstrate significant potential across the educational continuum, from foundational sciences to clinical reasoning. These systems provide scalable, personalized, and psychologically safe learning environments that address traditional limitations of human-led Socratic dialogue. However, critical challenges persist, including algorithmic bias, factual unreliability (hallucinations), data privacy concerns, and the paradoxical tension between surveillance requirements and psychological safety. Conclusions : AI represents a complementary, not replacement, technology for medical education. Successful integration requires simultaneous advancement of three pillars: institutional governance frameworks, longitudinal curriculum redesign, and comprehensive faculty development. The optimal model is a human-AI symbiosis that leverages AI for scalable inquiry while preserving human expertise for empathy, ethics, and complex clinical judgment.
