ARTIFICIAL INTELLIGENCE–ENABLED PERSONALIZED LEARNING IN MEDICAL EDUCATION: PEDAGOGICAL FOUNDATIONS, EDUCATIONAL OUTCOMES, AND IMPLEMENTATION CHALLENGES
Keywords:
artificial intelligence, personalized learning, medical education, adaptive learning, learning analytics, clinical reasoning, digital educationAbstract
Artificial intelligence (AI) has emerged as a transformative force in medical education, enabling personalized, adaptive, and data-driven learning environments. This comprehensive review synthesizes recent evidence on AI-enabled personalized learning systems, including intelligent tutoring platforms, adaptive assessments, learning analytics, and generative AI applications. The review examines pedagogical foundations, reported educational outcomes, ethical considerations, and institutional challenges associated with AI integration. Findings suggest that AI-supported personalization improves learner engagement, knowledge retention, and clinical reasoning when embedded within sound educational frameworks. However, issues related to transparency, data governance, faculty preparedness, and equity remain significant. The article concludes with strategic recommendations for responsible adoption and future research directions.
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