Research Article
Explainable Adaptive E-Learning Approach for Personalized Learning Contents Recommendation in an Intelligent E-Learning Environment
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 92 |
| Published: March 2026 |
| Authors: Omayma Meddeb, Mounir Zrigui |
10.5120/ijca2026926520
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Omayma Meddeb, Mounir Zrigui . Explainable Adaptive E-Learning Approach for Personalized Learning Contents Recommendation in an Intelligent E-Learning Environment. International Journal of Computer Applications. 187, 92 (March 2026), 12-20. DOI=10.5120/ijca2026926520
@article{ 10.5120/ijca2026926520,
author = { Omayma Meddeb,Mounir Zrigui },
title = { Explainable Adaptive E-Learning Approach for Personalized Learning Contents Recommendation in an Intelligent E-Learning Environment },
journal = { International Journal of Computer Applications },
year = { 2026 },
volume = { 187 },
number = { 92 },
pages = { 12-20 },
doi = { 10.5120/ijca2026926520 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A Omayma Meddeb
%A Mounir Zrigui
%T Explainable Adaptive E-Learning Approach for Personalized Learning Contents Recommendation in an Intelligent E-Learning Environment%T
%J International Journal of Computer Applications
%V 187
%N 92
%P 12-20
%R 10.5120/ijca2026926520
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The growing adoption of e-learning platforms in higher education has revealed the limitations of traditional systems, which often provide limited personalization and lack transparency in their decision-making processes. Although Artificial Intelligence (AI) and recommendation systems enable adaptive learning, many existing solutions operate as black boxes, reducing trust and acceptance among learners and instructors. This paper proposes a conceptual framework for an explainable adaptive e-learning system that integrates Learning Analytics, adaptive recommendation mechanisms, and Explainable Artificial Intelligence (XAI). The framework aims to personalize learning paths based on learner interaction data while providing understandable explanations for the generated recommendations. An illustrative usage scenario is presented to highlight how explainability can support both learners and instructors. This work represents an initial step toward transparent, human-centered, and adaptive e-learning systems.
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