PERSONALIZED HYBRID LEARNING THROUGH ARTIFICIAL INTELLIGENCE AND PREDICTIVE ANALYTICS: A SYSTEMATIC LITERATURE REVIEW
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Artificial Intelligence, Hybrid Learning, Personalized Learning, Predictive analytics
Abstract
Contemporary learning environments are increasingly complex, requiring adaptive, data-driven, and responsive instructional approaches. This study systematically examines the integration of artificial intelligence (AI) and predictive analytics in personalized hybrid learning and explores its contributions to educational science. A systematic literature review was conducted on peer-reviewed studies published between 2020 and 2025, sourced from Scopus, Web of Science, ERIC, and Google Scholar using defined inclusion and exclusion criteria. The selected studies were analyzed through thematic synthesis to identify key patterns, strategies, and outcomes. The findings show that integrating AI and predictive analytics enhances learning effectiveness by enabling adaptive content delivery, early identification of learning risks, and data-informed instructional decision-making in hybrid learning environments. The novelty of this study lies in its integrative perspective, positioning AI and predictive analytics as a unified framework for personalized hybrid learning rather than separate tools. Furthermore, the review highlights the potential of predictive insights derived from learner data to support responsive, equitable, and sustainable learning designs. Overall, this study suggests that strategic AI-driven personalization and predictive analytics can foster pedagogical innovation, strengthen evidence-based practices, and support the transformation of digital and hybrid learning systems.
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Copyright (c) 2025 Riska Meisyi Putri, Mardalena Mardalena, Zain Nizam
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