Explainable AI for Hyper-Personalized Learning: Personalized Intelligent Tutoring Systems

DOI

10.2991/978-94-6239-713-2_53How to use a DOI?

Keywords

XAI; Hyper-personalized learning; Intelligent Tutoring Systems (ITS); Learning analytics; Interpretable machine learning; Shapley; LIME; Transparency; Trust; Governance

Abstract

Artificial Intelligence (AI) plays a vital role in facilitating an Intelligent Tutoring System. XAI-based personalised Intelligent Tutoring Systems (ITS) for lifelong learning are transforming education by offering adaptive and customised learning experiences. But the black-box nature of many AI learning systems makes it difficult to understand how decisions are made. This dearth of transparency diminishes user trust and raises concerns about the ethical and moral justification of using such systems. Particularly sensitive student information and high-stakes suggestions are of concern. This paper proposes a multi-modal framework that integrates ITS. The approach analyzes multiple learner signals, including keystroke patterns and facial expression signals, to better understand learner behavior and provide interpretable explanations for the system’s decision.[1] The study describes the existing gaps that constitute a layered system architecture, models cases in field scenarios, addresses privacy and prejudice, and defines a roadmap for deploying previously workable ITS solutions.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shivani Sharma
AU  - O. P. Rishi
PY  - 2026
DA  - 2026/06/25
TI  - Explainable AI for Hyper-Personalized Learning: Personalized Intelligent Tutoring Systems
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 717
EP  - 730
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_53
DO  - 10.2991/978-94-6239-713-2_53
ID  - Sharma2026
ER  -