Artificial intelligence (AI)-powered adaptive learning systems (ALS) offer individualised learning experiences catered to the requirements of each individual student, marking a revolutionary development in the field of education. This study examines the mutually beneficial link between AI and adaptive learning, emphasising how both have the power to completely transform conventional educational approaches. ALS addresses the different learning demands of students by dynamically modifying instructional material, tempo, and resources based on learner responses. This approach departs from the one-size-fits-all methodology of traditional education. Artificial intelligence (AI)-powered computers improve ALS by analysing massive volumes of data, allowing for real-time adjustments and student-specific feedback. AI also makes scalability and accessibility possible, allowing cloud-based and online systems to reach a larger student population. To guarantee the moral and just use of AI-driven ALS, issues including algorithmic bias, data protection, and the requirement for teacher professional development must be resolved. The study covers the theoretical underpinnings of AI and adaptive learning, looks at their implementations, and talks about the advantages and difficulties of combining the two. Methodologically, secondary data from research papers, journals, and academic publications were used in a documentary analytic technique. In the future, developments in artificial intelligence (AI) and machine learning will continue to influence educational methods, calling for support for the dynamics between teachers and students as well as ethical and legal frameworks. All things considered, the incorporation of AI into adaptive learning systems has the potential to produce more successful, welcoming, and student-focused learning environments that cater to the individual requirements of every learner.
Adaptive Learning Systems, Artificial Intelligence, Qualitative Approach
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