Abstract

Intelligent Tutoring Systems (ITS) have undergone dramatic development over the last 40 years. They now inhabit a unique area where the fields of artificial intelligence, cognitive science, and education converge. This paper reviews the design and development of an explainable AI-based ITS for hyper-personalized lifelong learning. It explores why explainability is crucial, what algorithms power such systems, and how they are architected. The review also draws on evidence regarding the impact that these systems can have on student learning in both formal and informal learning environments. Bloom's mastery learning, Vygotsky's zone of proximal development, and the constructivist approach to learning are key theoretical frameworks discussed. The algorithms examined include Bayesian Knowledge Tracing, Deep Knowledge Tracing, LIME, SHAP, collaborative filtering, and reinforcement learning. Based on foundational studies and recent empirical research, the review concludes that XAI-driven ITS can have a significant impact on learning outcomes when hyper-personalization is tailored to the needs of the individual learner and made transparent to both the learner and the teacher. Finally, the paper lists practical challenges and directions for future exploration.

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