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Intelligent Tutoring Systems: A Comprehensive Guide to Personalized Learning

Srisudha Garugu1, V. Bhanu Sri2, E. Shravani3, B. Karthik4

Assistant Professor, Department of Computer Science &Engineering (AIML), ACE Engineering College, Ankushapur, Ghatkesar Mandal, Medchal District, Telangana. – 501301, India

 

 

Abstract

Intelligent Tutoring Systems (ITS) are transforming education by delivering personalized and adaptive learning experiences tailored to individual student needs. These systems utilize cutting-edge advancements in machine learning (ML) and artificial intelligence (AI) to model student knowledge, predict learning outcomes, and provide customized feedback. This paper outlines the design and development of a web-based ITS integrating Bayesian Knowledge Tracing (BKT), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models to enhance learning outcomes. The ITS employs Flask for the backend and React.js for the frontend to deliver an intuitive and interactive user experience. Additionally, this document discusses the system’s architecture, implementation, and its broader implications for modern education systems

Keywords: Intelligent Tutoring Systems, Bayesian Knowledge Tracing, RNN, LSTM, Flask, React.js, personalized learning, student modeling, adaptive education.

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