Volume 18, Issue 3 (Autumn 2026)
nkums 2026, 18(3): 1-13 |
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Jafarzadeh Hesari M, Soleimanpouromran M, Abbasi Z. A Systematic Review of Artificial Intelligence Applications in Educational Management with a Focus on Medical Students. nkums 2026; 18 (3) :1-13
URL: http://journal.nkums.ac.ir/article-1-3422-en.html
URL: http://journal.nkums.ac.ir/article-1-3422-en.html
1- Ph.D. Candidate in Educational Management, Department of Educational Sciences, Boj.C., Islamic Azad University, Bojnord, Iran
2- Department of Educational Sciences, Boj.C., Islamic Azad University, Bojnord, Iran ,soleimanpouromran@iau.ac.ir
3- Department of Midwifery, School of Nursing, North Khorasan University of Medical Sciences, Bojnord, Iran
2- Department of Educational Sciences, Boj.C., Islamic Azad University, Bojnord, Iran ,
3- Department of Midwifery, School of Nursing, North Khorasan University of Medical Sciences, Bojnord, Iran
Abstract: (52 Views)
Introduction: In recent years, artificial intelligence (AI) has emerged as a transformative force in medical education, particularly in optimizing educational management and students' outcomes. Although the proliferation of data-driven tools has pivoted systems toward evidence-based decision-making, the current body of literature remains fragmented, necessitating a rigorous synthesis. This study systematically reviews AI applications in educational management and evaluates their impact on medical students' academic performance.
Methods: This systematic review was conducted in accordance with the PRISMA 2020 guidelines. A comprehensive search was conducted across Scopus, PubMed, Web of Science, ERIC, IEEE Xplore, and Google Scholar for studies published between 2020 and 2025. Following rigorous screening and quality appraisal (using CASP, JBI, and AMSTAR-2), 35 studies were selected from 542 initial records. Data were analyzed via thematic synthesis.
Results: The findings indicated that AI applications were categorized into three tiers: learner-centered, data-driven, and strategic. Adaptive learning systems yielded a >20% improvement in theoretical scores, enhanced self-directed learning, and reduced assessment-related anxiety. Data-driven models significantly improved the prediction of academic underachievement and clinical skill proficiency. At the strategic level, intelligent decision-support systems optimized educational planning and policy-making. Nevertheless, challenges, such as digital inequality and ethical concerns, including data privacy and algorithmic bias, remain significant barriers.
Conclusion: The evidence suggests that AI integration in medical educational management can substantially enhance academic performance and facilitate evidence-based educational decision-making; however, addressing ethical challenges and infrastructure disparities remains crucial for its sustainable implementation.
Methods: This systematic review was conducted in accordance with the PRISMA 2020 guidelines. A comprehensive search was conducted across Scopus, PubMed, Web of Science, ERIC, IEEE Xplore, and Google Scholar for studies published between 2020 and 2025. Following rigorous screening and quality appraisal (using CASP, JBI, and AMSTAR-2), 35 studies were selected from 542 initial records. Data were analyzed via thematic synthesis.
Results: The findings indicated that AI applications were categorized into three tiers: learner-centered, data-driven, and strategic. Adaptive learning systems yielded a >20% improvement in theoretical scores, enhanced self-directed learning, and reduced assessment-related anxiety. Data-driven models significantly improved the prediction of academic underachievement and clinical skill proficiency. At the strategic level, intelligent decision-support systems optimized educational planning and policy-making. Nevertheless, challenges, such as digital inequality and ethical concerns, including data privacy and algorithmic bias, remain significant barriers.
Conclusion: The evidence suggests that AI integration in medical educational management can substantially enhance academic performance and facilitate evidence-based educational decision-making; however, addressing ethical challenges and infrastructure disparities remains crucial for its sustainable implementation.
Type of Study: Review Article |
Subject:
Basic Sciences
Received: 2026/01/13 | Accepted: 2026/07/1 | Published: 2026/09/29
Received: 2026/01/13 | Accepted: 2026/07/1 | Published: 2026/09/29
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