Volume 20, Issue 3 (Autumn 2025)
jmed 2025, 20(3): 1324-1327 |
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heydari S, alavi Z S. Intelligent Adaptive Learning: An AI-Powered Revolution in Medical Education. jmed 2025; 20 (3) :1324-1327
URL: http://jmed.ssu.ac.ir/article-1-1574-en.html
URL: http://jmed.ssu.ac.ir/article-1-1574-en.html
Shahid Sadoughi University of Medical Sciences, Yazd, Iran , zohrehsalavi@gmail.com
Abstract: (28 Views)
Abstract
Medical education in the era of information explosion and rapid technological advancements faces unprecedented challenges. On one hand, the exponentially growing body of medical knowledge, and on the other hand, the need to train specialists with advanced cognitive and practical skills, has compelled educational systems to reconsider traditional teaching methods. In this context, adaptive learning has emerged as a novel paradigm in medical education, attracting significant attention from researchers and educational policymakers.This study employs existing scientific evidence to provide a comprehensive examination of the theoretical foundations, emerging technologies, challenges, and future solutions regarding adaptive learning in medical education
Medical education in the era of information explosion and rapid technological advancements faces unprecedented challenges. On one hand, the exponentially growing body of medical knowledge, and on the other hand, the need to train specialists with advanced cognitive and practical skills, has compelled educational systems to reconsider traditional teaching methods. In this context, adaptive learning has emerged as a novel paradigm in medical education, attracting significant attention from researchers and educational policymakers.This study employs existing scientific evidence to provide a comprehensive examination of the theoretical foundations, emerging technologies, challenges, and future solutions regarding adaptive learning in medical education
Type of Study: letter to editor |
Subject:
Medical Education
Received: 2025/07/15 | Accepted: 2025/07/20 | Published: 2025/12/21
Received: 2025/07/15 | Accepted: 2025/07/20 | Published: 2025/12/21
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