A STUDY ON THE APPLICATION OF DEEP LEARNING AND MACHINE LEARNING IN ADAPTIVE INTELLIGENT TUTORING

Abstract

The Intelligent Tutoring Systems (ITS) are now regarded as one of the pillars of modern educational technology, offering a personalized experience in education, adapting to the needs of individual students. The paper addresses the use of ML algorithms in adaptive ITS and how they can be used to create better learning experiences, student interactions, and system efficiency. The paper will examine various machine learning methods, including supervised learning, reinforcement learning, and neural networks, which have been used in ITS. Also, it covers the issues, assessment techniques, and research directions of adaptive intelligent tutoring systems. The Intelligent Tutoring Systems (ITS) are an essential factor in providing individualized education with the help of adaptive learning technologies. Modern ITS has been greatly improved in adaptability and intelligence due to the combination of Machine Learning (ML) and Deep Learning. This paper examines the application of supervised, unsupervised, reinforcement, and deep learning methods in adaptive ITS. Decision Trees, SVM, and Random Forests are supervised models that are applicable in predicting student performance with an accuracy of up to 90. Unsupervised algorithms such as K-Means clustering can be used to define the behavioral patterns of learners to aid in personalized teaching. Reinforcement Learning is a process of maximising tutoring strategies by the use of reward-based policy improvements. Deep Learning methods, especially Deep Knowledge Tracing using LSTM, are effective in structuring temporal learning styles with a greater AUC of more than 0.85. The study puts emphasis on student modeling, involving knowledge tracing and learner profiling, in adaptive systems. Adaptive techniques are reviewed, such as dynamic feedback, content sequencing, which is personal, and real-time assessment. An integrated ITS architecture, based on the use of ML, includes student, tutoring, and analytics modules. The measures of evaluation are the learning gain, the rate of engagement, and the accuracy of prediction (RMSE, MAE). 20-25 percent learning increase by ML-based adaptive conditions when applied. Issues with implementation, such as data privacy and real-time scalability, are also discussed in the study. Afterwards Explainable AI model and multiple models for further personalization are used. All in all, ML-driven ITS proves to have great opportunities of scalable, data-driven, and exceptionally personalized educational system.