Authors : Dr. Mallika Natarajan

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/5n8n8wvb

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DOI : https://doi.org/10.38124/ijisrt/25nov1395

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid advancement of Artificial Intelligence (AI) in the field of education necessitates a profound redefinition of the educator’s function, transitioning their role from the primary purveyor of content to a strategic analyzer of data and facilitator of personalized learning experiences. Conventional, uniform grouping approaches inadequately exploit the extensive data accessible within AI-enhanced educational settings, thereby obstructing the provision of genuinely tailored instructional methodologies. This research introduces an innovative, data-centric framework that employs the K-Means clustering algorithm to establish highly optimized and homogeneous student cohorts, predicated on a variety of performance indicators, engagement behaviors, and learning characteristics. Through the application of K-Means, educators can transcend instinctive grouping strategies to discern specific, collective needs within micro-groups, thus facilitating the implementation of hyper-targeted interventions and resources.

Keywords : Instructor Role Redefinition, Data-Driven Strategies, K-Means Clustering, Student Grouping, Personalized Instruction, Learning Analytics, Adaptive Learning.

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