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Volume 18, Issue 4 (2025)
JMED 2025, 18(4): 119-139 |
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Mahdi R, Keykha A, Kaliisa R, Darabi F. Exploring applications of artificial intelligence in enhancing the quality of medical education: a mixed methods research synthesis. JMED 2025; 18 (4) :119-139
URL: http://edujournal.zums.ac.ir/article-1-2480-en.html
URL: http://edujournal.zums.ac.ir/article-1-2480-en.html
1- Department of Futures Studies, Institute for Cultural and Social Studies, Ministry of Science, Research and Technology, Tehran, Iran
2- Sharif Policy Research Institute, Sharif University of Technology, Tehran, Iran ,ahmad.keykha72@sharif.edu
3- Department of Education, University of Oslo, Oslo, Norway
4- Department of Educational Psychology, Islamic Azad University, Science and Research Branch, Tehran, Iran
2- Sharif Policy Research Institute, Sharif University of Technology, Tehran, Iran ,
3- Department of Education, University of Oslo, Oslo, Norway
4- Department of Educational Psychology, Islamic Azad University, Science and Research Branch, Tehran, Iran
Abstract: (198 Views)
Background & Objective: Academic systems are among the many spheres of human life highly influenced by artificial intelligence (AI). The idea of quality in medical education is changing as a result of AI-driven developments, creating both opportunities and difficulties. The purpose of this study is to investigate how AI might be used to improve the quality of medical education.
Materials & Methods: Mixed methods research synthesis was the approach taken. Relevant studies published in Science Direct, Springer, ERIC, Emerald, Sage Journals, Wiley Online Library, PubMed, and Google Scholar between 2015 and 2025 were found using targeted search terms. Quality was assessed through the Mixed Methods Appraisal Tool (MMAT) and selection process followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The final review included 49 studies that met the criteria. A model with eight dimensions of the quality of medical education was employed to analyze the data.
Results: The results were grouped into eight categories: mission and goals, organizational structure and governance, faculty members, students, teaching and learning processes, curricula, facilities, and research activities. AI was found to have a positive effect on all areas, with the most focus on faculty members (38 citations) and teaching-learning processes (36 citations). It was found that these themes were very important for making education better. By comparison, mission and objectives, and research activities received little reference (8 references each), indicating strategic and research-focused AI integration lacunae.
Conclusion: AI has the most potential to change how medical education is taught by using new teaching tools, better lesson plans, and personalized learning. But the fact that research and planning dimensions don't cover everything shows how important it is to do research and make policies with clear, well-defined goals. Balanced implementation of AI in all dimensions of quality is needed to bring sustainable and comprehensive transformations in medical education. The current study offers significant implications to educators, policymakers, and researchers for guiding AI-supported education reforms in the future.
Materials & Methods: Mixed methods research synthesis was the approach taken. Relevant studies published in Science Direct, Springer, ERIC, Emerald, Sage Journals, Wiley Online Library, PubMed, and Google Scholar between 2015 and 2025 were found using targeted search terms. Quality was assessed through the Mixed Methods Appraisal Tool (MMAT) and selection process followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The final review included 49 studies that met the criteria. A model with eight dimensions of the quality of medical education was employed to analyze the data.
Results: The results were grouped into eight categories: mission and goals, organizational structure and governance, faculty members, students, teaching and learning processes, curricula, facilities, and research activities. AI was found to have a positive effect on all areas, with the most focus on faculty members (38 citations) and teaching-learning processes (36 citations). It was found that these themes were very important for making education better. By comparison, mission and objectives, and research activities received little reference (8 references each), indicating strategic and research-focused AI integration lacunae.
Conclusion: AI has the most potential to change how medical education is taught by using new teaching tools, better lesson plans, and personalized learning. But the fact that research and planning dimensions don't cover everything shows how important it is to do research and make policies with clear, well-defined goals. Balanced implementation of AI in all dimensions of quality is needed to bring sustainable and comprehensive transformations in medical education. The current study offers significant implications to educators, policymakers, and researchers for guiding AI-supported education reforms in the future.
Keywords: artificial intelligence (AI), medical education, educational technology, quality improvement, mixed methods synthesis
Article Type : Review |
Subject:
Medical Education
Received: 2025/05/13 | Accepted: 2025/10/26 | Published: 2025/11/19
Received: 2025/05/13 | Accepted: 2025/10/26 | Published: 2025/11/19
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