Frontiers of Artificial Intelligence for Personalized Learning in Higher Education: A Systematic Review of Leading Articles

Abstract

Artificial Intelligence (AI) is reshaping higher education by enabling personalized learning (PL) and enhancing teaching and learning practices. To examine global research trends, pedagogical paradigms, equity and sustainability considerations, instructional strategies, learning outcomes, and interdisciplinary collaboration, this study systematically reviewed 29 articles indexed in the Social Sciences Citation Index (SSCI) Q1, representing the top 25% of cited articles, published between January 2020 and December 2024 in the Web of Science database. Results indicate that AI-PL research is concentrated in Asia, particularly China, and predominantly situated within education and computer science. Quantitative designs prevail, often complemented by qualitative insights, with supervised machine learning as the most common algorithm. While constructivist principles implicitly guide most studies, explicit theoretical grounding improves AI-pedagogy alignment and educational outcomes. AI demonstrates potential to enhance instructional approaches such as PBL, STEAM, gamification, and UDL, and to foster higher-order skills, yet uncritical use may undermine learner autonomy. Systematic attention to equity and SDG-related objectives remains limited. Emerging interdisciplinary collaborations show promise but are not yet fully institutionalized, constraining integrative system design. These findings underscore the need for stronger theoretical framing, alignment of AI with pedagogical and societal imperatives, and professional development to enhance educators’ AI literacy. Coordinated efforts among academia, industry, and policymakers are essential to develop scalable, context-sensitive AI solutions that advance inclusive, adaptive, and transformative higher education.

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