Exploring AI-powered adaptive learning systems and their implementation in educational settings: A systematic literature review

Raúl Andrés Pinela-Cárdenas, Huber Echeverría-Vásquez, Dennis Alfredo Peralta-Gamboa (), Evelin Arteaga-Arcentales () and Jefferson Mendoza-Carrera ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 4, 832-842

Abstract: The goal of this study was to investigate how AI-powered adaptive learning systems are developed and applied in educational environments, with an emphasis on how these systems improve student results and learning customization. A thorough review of the literature was conducted to examine significant research that uses AI algorithms such as neural networks and support vector machines to modify course materials and evaluate student performance in real-time. This review shows that AI-driven adaptive learning systems enhance student engagement and academic performance, especially in online and STEM learning environments. However, insufficient infrastructure, computational biases, and technological constraints have hindered wider adoption. This study suggests that to improve the integration of AI in education, there should be more international collaboration and the creation of ethical frameworks to address existing restrictions.

Keywords: Adaptive learning; artificial intelligence in education; learning personalization; personalized education. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:4:p:832-842:id:7961

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