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Vol.15, No.2, May 2026. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
From AI to Engagement: Tracing the Evolution of Personalized Learning in Higher Education through Bibliometric Insights
Lim Seong Pek, Rita Wong Mee Mee, Fatin Syamilah Che Yob, Haida Umiera Hashim, Jun S. Camara, Nuril Mufidah
© 2026 Rita Wong Mee Mee, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 15, Issue 2, Pages 1442-1454, ISSN 2217-8309, DOI: 10.18421/TEM152-41, May 2026.
Received: 23 May 2025.
Abstract:
Personalized learning, which aims to adapt instruction to each student's needs, preferences, and performance, has become a crucial strategy in higher education. This bibliometric analysis uses 215 peer-reviewed publications that are indexed in the Web of Science database to map the intellectual and thematic structure of research on personalized learning in higher education from 2015 to 2024. This study identifies important authors, organizations, journals, and nations that contribute to the area through the use of performance analysis, co-citation, and keyword co-occurrence methodologies via VOSViewer. The findings show that learning analytics, adaptive learning technologies, and Artificial Intelligence (AI) are rapidly influencing research, with recent studies placing a strong emphasis on generative AI tools like ChatGPT. Digital transformation, pedagogical frameworks, data-driven personalization, educational innovation, generative AI ethics, and learner-centered design are the six main threads that emerge from thematic clusters. The results demonstrate a paradigm shift in AI-mediated learning from conceptual to applied and ethical considerations. Expanded integration of traditional models, such as the Technology Acceptance Model, with more recent concepts, including learner autonomy in AI ecosystems, is necessary for theoretical reasons. From a practical standpoint, the study emphasizes the necessity of developing ethical policies, educating educators, and designing teaching materials based on evidence. Notably, by supporting inclusive, egalitarian, and customized learning environments that close digital divides and promote global educational equity, this research supports Sustainable Development Goal 4 (Quality Education). With its roadmap for future research, policy, and practice in the face of rapid technological change, this study strategically advances personalized learning in higher education.
Keywords – Personalized learning, higher education, learning analytics, education technology, student engagement. |
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