Authors - Mustafa Icel, Ochilbek Rakhmanov, Ergul Gunerhan, Muhammad Qasim Abstract - Artificial intelligence driven adaptive learning systems progressively operate as knowledge management platforms by collecting, refining, and using learner knowledge to personalize instruction. However, empirical evidence demonstrating how managed knowledge translates into measurable student achievement remains as a question to answer. This study examines the effective ness of AI driven adaptive learning as a knowledge management system in a high school setting. Using de-identified archival data from 182 students across three academic years, the study explores relationships among AI-managed knowledge mastery, engagement, course performance, and standardized assessment out comes. Learning analytics techniques, including descriptive statistics and Pear son correlation analysis, were employed to examine knowledge–performance re lationships. Predictive modeling using multivariable linear regression and Ran dom Forest classification was performed to assess the extent to which knowledge management indicators predict end-of-course achievement and performance lev els. Results indicate that final knowledge mastery is moderately associated with standardized assessment outcomes and is a stronger predictor of achievement than time-on-task alone. While predictive models demonstrate modest accuracy, findings suggest that AI driven knowledge management supports student achievement when integrated within instructional contexts.
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