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Review on Explainable AI–Driven Intelligent Tutoring System for Hyper-Personalized Lifelong Learning

by Shivani Sharma, Om Prakash Rishi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 129
Year of Publication: 2026
Authors: Shivani Sharma, Om Prakash Rishi
10.5120/ijcad9695f624bf9

Shivani Sharma, Om Prakash Rishi . Review on Explainable AI–Driven Intelligent Tutoring System for Hyper-Personalized Lifelong Learning. International Journal of Computer Applications. 187, 129 ( Jul 2026), 40-46. DOI=10.5120/ijcad9695f624bf9

@article{ 10.5120/ijcad9695f624bf9,
author = { Shivani Sharma, Om Prakash Rishi },
title = { Review on Explainable AI–Driven Intelligent Tutoring System for Hyper-Personalized Lifelong Learning },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2026 },
volume = { 187 },
number = { 129 },
month = { Jul },
year = { 2026 },
issn = { 0975-8887 },
pages = { 40-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number129/review-on-explainable-aidriven-intelligent-tutoring-system-for-hyper-personalized-lifelong-learning/ },
doi = { 10.5120/ijcad9695f624bf9 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-08-01T02:11:32.244151+05:30
%A Shivani Sharma
%A Om Prakash Rishi
%T Review on Explainable AI–Driven Intelligent Tutoring System for Hyper-Personalized Lifelong Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 129
%P 40-46
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Intelligent tutoring systems explainable AI hyper-personalization lifelong learning knowledge tracing student modelling Bayesian knowledge tracing deep learning academic performance.

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