The digital revolution has catalyzed the transformation of nearly every facet of society, and education is no exception. With the increasing integration of Artificial Intelligence (AI) in educational systems, adaptive learning is emerging as a transformative approach to delivering personalized education. Unlike traditional methods that employ a "one-size-fits-all" strategy, adaptive learning platforms leverage AI algorithms to analyze learner behavior, track performance, and adjust content dynamically to fit the learner's pace, style, and needs. This paper presents a comprehensive review of adaptive learning as an AI-driven model, exploring its theoretical underpinnings, practical implementations, and potential impacts on personalized education. The analysis includes a comparative study of adaptive learning versus traditional methods, highlighting increased learner engagement, improved outcomes, and higher retention rates. Furthermore, the study addresses various AI techniques such as machine learning, natural language processing, and neural networks, which facilitate personalization. Additionally, the review delves into the challenges of implementing adaptive learning systems, such as data privacy concerns, algorithmic bias, infrastructure limitations, and the digital divide. It also discusses the role of educators in an AI-powered learning ecosystem and explores the ethical implications of relying heavily on automated systems. By drawing from real-time data, visual representations, case studies, and academic literature, this review envisions a future where AI redefines the learning experience, making education more inclusive, accessible, and effective. The findings support the thesis that adaptive learning is not merely a supplementary tool but a foundational shift in how education can be structured in the digital age.
Juyal et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: