A Critical Glance at Adaptive Learning Systems Using Artificial Intelligence: A Systematic Review and Qualitative Synthesis of Contemporary Research Literature

Abstract

This study aims to critically examine the current research on AI-powered adaptive learning systems by synthesizing studies to identify trends, gaps, and challenges. It also explores the applications, benefits, challenges, and future directions of these systems in education. The research design employs a systematic review and qualitative thematic synthesis following PRISMA guidelines. Data collection involves a comprehensive literature search across databases such as ERIC, JSTOR, IEEE Xplore, Google Scholar, Web of Science, and Scopus. Inclusion criteria focus on peer-reviewed articles and high-quality grey literature from the past ten years. Data analysis includes coding and thematic mapping to integrate findings into a comprehensive narrative, ensuring rigor through triangulation, peer debriefing, and reflexivity. The findings reveal significant themes related to the role of AI in adaptive learning, including machine learning algorithms, natural language processing, and AI-driven data analysis. Applications of adaptive learning systems are demonstrated in personalized learning pathways, adaptive assessments, intelligent tutoring systems, and support for diverse learning needs. Case studies highlight the effectiveness of these systems in enhancing student engagement and learning outcomes. This study provides a comprehensive overview of the potential of AI-powered adaptive learning systems to transform education. It identifies significant benefits such as improved learning outcomes, increased engagement, scalability, and cost-effectiveness. The study also addresses challenges like data quality, ethical considerations, and institutional resistance, providing a balanced view of the current landscape. AI-powered adaptive learning systems have innovative potential in personalizing and improving educational experiences. While the benefits are significant, addressing challenges related to data quality, ethical considerations, and educator support is crucial. Future research should focus on long-term impacts, ethical implications, and integrating emotional and social learning to create a holistic educational environment.

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