A Study On The Application Of Machine Learning In Adaptive Intelligent Tutoring Systems

Abstract

This paper investigates how ML techniques could be applied to design and enhance AITS with the aim of offering personalized, data-driven learning environments.   Growing need for smart educational tools motivates innovative ML integration into tutoring systems to meet different student demands, increase participation, and improve academic performance.   The study evaluates the efficacy of significant ML algorithms—including supervised learning, RL, and DL—within the context of learner modeling, adaptive content delivery, and real-time feedback systems.   A prototype ML-AITS framework was developed and tested across multiple learner groups, comparing traditional education, basic adaptive systems, and fully adaptable ML-based systems.   Quantitative research reveals that ML-AITS substantially surpasses traditional methods in key areas such learner participation, instructional effectiveness, and learning results.   For instance, pupils using ML-AITS exhibited up to a 16.9% rise in post-test scores and more active measures compared to their counterparts.   Comprising learner profile, adaptive content delivery, real-time assessment, performance analytics, and continuous learning layers, the proposed five-layered ML-AITS architecture forms a dynamic and intelligent ecosystem competent of self-improvement.   The findings validate the potential of machine learning to change digital education by means of intelligent personalisation and adaptive feedback loops.   Our work contributes to the growing field of educational technology by providing a scalable and efficient ML-driven tutoring system.   It also offers a foundation for future studies on more general applications in multilingual and multicultural educational environments, emotionally intelligent systems, and NLP integration.