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Mapping Personalized Learning in Medical Education: A Meta-Synthesis of Artificial Intelligence Applications
Ava Taghavi Monfared1, Maryam Hojati2, Zohreh Farahmandpour3
1Department of Educational Administration and Planning, Faculty of Psychology and Education, University of Tehran, Tehran, Iran.
Summary
Artificial Intelligence (AI) in medical education shifts learning to personalized models. Student-centered AI applications are most prevalent, enhancing engagement and outcomes in adaptive learning environments.
Area of Science:
- Medical Education
- Artificial Intelligence
- Learning Sciences
Background:
- Advancements in Artificial Intelligence (AI) are transforming medical education.
- Traditional instructional design is shifting towards personalized and adaptive learning models.
- Existing evidence on AI in medical education is limited and fragmented, necessitating a comprehensive synthesis.
Purpose of the Study:
- To synthesize the existing evidence on Artificial Intelligence applications in medical education.
- To identify key domains and trends of AI integration in medical training.
- To propose a conceptual framework for AI-supported personalized medical education.
Main Methods:
- Employed a four-phase meta-synthesis framework.
- Conducted a systematic literature search across 12 major databases (2010-2025).
- Evaluated methodological quality using the Critical Appraisal Skills Programme (CASP) and assessed coding reliability (0.81).
Main Results:
- Included 16 studies meeting inclusion criteria and quality thresholds.
- Identified five principal application domains: faculty (21%), student (28%), learning process (15%), curriculum (13%), and assessment (23%).
- Student-related applications were the most prominent, emphasizing learner-centered personalization.
Conclusions:
- Artificial Intelligence integration offers a transformative model for individualized medical education.
- AI facilitates adaptive learning, dynamic assessment, and data-driven instruction.
- This synthesis provides a framework to guide policy, research, and implementation of AI in personalized medical education.
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