Journal of Intelligent Strategic Management

Journal of Intelligent Strategic Management

Design and explanation of a comprehensive personalized e-learning model based on artificial intelligence in the medical education system with a case study of Tehran University of Medical Sciences

Document Type : Original Article

Authors
Sepideh Mohammadpour 1
Mehdi Keramatpour 2
MohammadAmin Torabi 3
1 Department of Managemen, Ro.C., Islamic Azad university, Roudehen, Iran.
2 Department of industrial engineering, Ro.C., Islamic Azad university, Roudehen, Iran.
3 Department of Management, Nabi Akram (PBUH) Higher Education Institute, Tabriz, Iran.
10.87453/bumara.2026.373601.4206
Abstract
Higher education systems, particularly medical education, have recently faced a set of fundamental challenges, including the inefficiency of standardized instructional models, insufficient responsiveness to learner differences, limitations in educational data analytics, and the inadequate utilization of emerging technologies, especially artificial intelligence. In many universities, including Tehran University of Medical Sciences, e-learning systems are largely based on static and non-adaptive content delivery, which reduces learning effectiveness, lowers student motivation, and fails to align with diverse clinical and cognitive needs. In contrast, the emergence of artificial intelligence has enabled a shift toward personalized learning systems; however, the lack of comprehensive and localized models remains a significant gap in this field.This study aims to design a comprehensive model for AI-based personalized e-learning at Tehran University of Medical Sciences. The proposed model integrates learning theories, data analytics, and intelligent technologies to provide a framework for adapting content, learning paths, and feedback according to individual learner characteristics, while also considering contextual factors such as educational culture, data governance, and technological infrastructure.The research adopts a sequential exploratory mixed-methods design. In the qualitative phase, data were collected through expert interviews and analyzed using thematic analysis with open, axial, and selective coding. In the quantitative phase, the extracted model was tested using a researcher-developed questionnaire and structural equation modeling among students of Tehran University of Medical Sciences.The findings indicate that learning personalization is simultaneously influenced by indigenous, technological, and institutional factors and plays a central role in enhancing intelligent learning strategies. However, achieving final outcomes requires strengthening governance structures, organizational culture, and user trust.
Keywords
e-learning
personalized learning
artificial intelligence in education
medical education
adaptive learning
Subjects
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Journal of Intelligent Strategic Management
Volume 5, Issue 2 - Serial Number 14
Summer 2026
Pages 771-792