Department of English Language, Am.C., Islamic Azad University, Amol, Iran.
Abstract: (32 Views)
Objective: The present systematic review synthesizes peer-reviewed research published between 2019 and 2026 to examine how personalized learning has been conceptualized, implemented, and redefined within AI-supported educational environments.
Methods: Following the PRISMA 2020 guidelines, relevant studies were systematically identified across four major academic databases (Scopus, Web of Science, IEEE Xplore, and ERIC) and analyzed to explore conceptual definitions, technological innovations, pedagogical applications, ethical considerations, and emerging research directions. In addition to synthesizing established evidence, the review incorporates recent advances in generative AI, learning analytics, explainable AI, multimodal learning environments, and human–AI collaboration, thereby extending previous reviews and providing a more contemporary understanding of AI-enhanced personalized learning.
Results: The findings demonstrate that personalized learning is increasingly evolving toward intelligent, adaptive ecosystems in which educators and AI systems operate as complementary partners to support individualized learning while maintaining human oversight and pedagogical integrity. Despite substantial technological progress, persistent challenges (including conceptual ambiguity, algorithmic bias, data privacy, transparency, equitable implementation, and teacher preparedness) continue to limit widespread adoption and effectiveness. Building upon the synthesized evidence, this review proposes an Integrated Human-Centered AI Personalized Learning Framework that unifies the essential pedagogical, technological, and ethical dimensions of personalized learning into a comprehensive conceptual model.
Conclusion: Overall, AI-supported personalized learning has progressed from an emerging educational innovation to a strategically important component of contemporary education. Its future success will depend not only on advances in AI but also on the quality of collaboration among educators, learners, institutions, and policymakers.
Keywords:
Methods: Following the PRISMA 2020 guidelines, relevant studies were systematically identified across four major academic databases (Scopus, Web of Science, IEEE Xplore, and ERIC) and analyzed to explore conceptual definitions, technological innovations, pedagogical applications, ethical considerations, and emerging research directions. In addition to synthesizing established evidence, the review incorporates recent advances in generative AI, learning analytics, explainable AI, multimodal learning environments, and human–AI collaboration, thereby extending previous reviews and providing a more contemporary understanding of AI-enhanced personalized learning.
Results: The findings demonstrate that personalized learning is increasingly evolving toward intelligent, adaptive ecosystems in which educators and AI systems operate as complementary partners to support individualized learning while maintaining human oversight and pedagogical integrity. Despite substantial technological progress, persistent challenges (including conceptual ambiguity, algorithmic bias, data privacy, transparency, equitable implementation, and teacher preparedness) continue to limit widespread adoption and effectiveness. Building upon the synthesized evidence, this review proposes an Integrated Human-Centered AI Personalized Learning Framework that unifies the essential pedagogical, technological, and ethical dimensions of personalized learning into a comprehensive conceptual model.
Conclusion: Overall, AI-supported personalized learning has progressed from an emerging educational innovation to a strategically important component of contemporary education. Its future success will depend not only on advances in AI but also on the quality of collaboration among educators, learners, institutions, and policymakers.
Keywords:
Keywords: AI, personalized learning, human centered AI, intelligent tutoring systems, learning analytics, customized learning, personalized adaptive learning, systematic review
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This work is licensed under a Creative Commons — Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). |
This work is licensed under a Creative Creative Commons — Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)