Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2025)

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Personalizing E-Learning Through Adaptive Learning Systems

Authors
Kawtar Yaqine1, *, Mohammed Sefian Lamarti1, Mohamed Khaldi2
1Applied Mathematics and Computer Science Team, Normal Higher School, Abdelmalek Essaadi University, Tetouan, Morocco
2Laboratory of Information Technologies and System Modeling, Faculty of Science, Tetouan, Morocco
*Corresponding author. Email: kawtar.yaqine@etu.uae.ac.ma
Corresponding Author
Kawtar Yaqine
Available Online 2 April 2026.
DOI
10.2991/978-94-6239-634-0_17How to use a DOI?
Keywords
Adaptive learning systems; personalization; e-learning; artificial intelligence; learning style; learner modeling; intelligent tutoring
Abstract

The advent of digital and educational technologies has led to the emergence of ever more varied online learning environments. Nevertheless, most traditional e-learning platforms remain based on a uniform pedagogy, unable to respond to the specificities of each learner. In this context, adaptive learning systems (AAS) represent a significant advance: they exploit learner models (cognitive profile, learning style, skill level) as well as adaptivity engines to offer a personalized journey and content. This article first presents the theoretical foundations of AAS (learner models, technical architectures, pedagogical approaches), then offers a critical review of the major works published over the last ten years. Finally, a comparative study of five current systems is conducted according to criteria such as the type of personalization, the granularity of content, evaluation methods, and interactivity. At the end of this analysis, we detail the technical and pedagogical challenges still to be overcome, and propose research perspectives, including artificial intelligence and massive data analysis, to strengthen the effectiveness of SAA.

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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Volume Title
Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2025)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
2 April 2026
ISBN
978-94-6239-634-0
ISSN
2667-128X
DOI
10.2991/978-94-6239-634-0_17How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Kawtar Yaqine
AU  - Mohammed Sefian Lamarti
AU  - Mohamed Khaldi
PY  - 2026
DA  - 2026/04/02
TI  - Personalizing E-Learning Through Adaptive Learning Systems
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2025)
PB  - Atlantis Press
SP  - 204
EP  - 215
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6239-634-0_17
DO  - 10.2991/978-94-6239-634-0_17
ID  - Yaqine2026
ER  -