Systematic Literature Review on Artificial Intelligence-Driven Personalized Learning

• Published in: International journal of advanced computer science & applications Vol. 16; no. 6
• Main Authors: Inuwa, Anas Usman, Sulaiman, Shahida, Samsudin, Ruhaidah
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2025
• Subjects: Artificial intelligence; Colleges & universities; Customization; Deep learning; Education; Kindergarten; Literature reviews; Machine learning; Personalized learning; Publication output; Systematic review
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2025.0160636

Artificial Intelligence (AI) is widely used in various contexts, including education at different levels, such as K-12 (kindergarten through 12th grade) and higher learning. The impact of AI in education is becoming increasingly significant, making the academic sphere more effective, personalized, global, context-intensive, and asynchronous. Despite the publication of several systematic literature reviews, mapping studies, and reviews on the use of AI in education, there is still a lack of reviews focusing on personalized learning (PL) frameworks, models, and approaches at various levels especially the pre-university level for Science, Technology, Engineering, and Mathematics (STEM) subjects. To address this gap, our work presents a systematic literature review of AI-driven PL models, frameworks, and approaches published over the past ten years from 2013 to 2023, extracted from the Scopus database. This review focuses on the AI techniques used, personalized learning elements, components, attributes, and the possibility of replicating the technique in pre-university level studies, and gaps or prospects that will attract further research. The study reviewed 69 articles, downloaded via the Scopus database, and reported the most used AI techniques, PL components or factors, trends, and prospects for future research. The results show that most existing studies focus on higher learning that requires further research at the pre-university level. In addition, machine learning and deep learning are identified as the most suitable and frequent techniques besides other technologies, knowledge delivery, learners’ needs, behavior and interest as the most required components for personalized systems in diverse fields. In terms of publication output by country, the study indicates that Switzerland, USA, UK, and China are leading contributors to PL research. Thus, this study calls for further research on AI-driven personalized learning that thoughtfully integrates educational theories, subject-specific content, and industry needs to enhance outcomes and learner satisfaction.