Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)

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The Impact Of Innovative AI-based Approaches on Learner Engagement in Science At University

Authors
Mohamed Benfarha1, *, Lamarti Sefian Mohammed1, Khaldi Mohamed2
1A research team in Computer Science and University Pedagogical Engineering, (LASAD) Higher Normal School of Tétouan, Abdel Malek Essaadi University, Tétouan, Morocco
2University Pedagogical Engineering, Équipe S2IPU, Higher Normal School of Tétouan, Abdelmalek Essaadi University, Tétouan, Morocco
*Corresponding author. Email: m.benfarha@uae.ac.ma
Corresponding Author
Mohamed Benfarha
Available Online 20 June 2025.
DOI
10.2991/978-2-38476-408-2_21How to use a DOI?
Keywords
Adaptive learning systems; artificial intelligence; personalization; engagement
Abstract

Adaptive learning systems (ALS), powered by artificial intelligence, represent an innovative approach to increasing student engagement in science at the university level. These systems, by analyzing the individual performance of students, adapt the content and pace of learning to their specific needs. This personalization of the learning experience results in increased motivation, reduced frustration and improved academic performance. However, the implementation of ALS presents obstacles encountered. Despite these challenges, ESLs offer considerable potential to revolutionize science education and optimize student engagement.

Our study, presented in this chapter, explores the impact of AI-based ESLs on student engagement in science. The results of our study demonstrate that ESLs offer remarkable potential for improving learner engagement in science. To ensure equitable and effective use of these technologies, it is essential to take into account the challenges discussed and ensure that ALS are implemented in a responsible and transparent manner.

Copyright
© 2025 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 2024)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
20 June 2025
ISBN
978-2-38476-408-2
ISSN
2667-128X
DOI
10.2991/978-2-38476-408-2_21How to use a DOI?
Copyright
© 2025 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  - Mohamed Benfarha
AU  - Lamarti Sefian Mohammed
AU  - Khaldi Mohamed
PY  - 2025
DA  - 2025/06/20
TI  - The Impact Of Innovative AI-based Approaches on Learner Engagement in Science At University
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)
PB  - Atlantis Press
SP  - 278
EP  - 290
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-2-38476-408-2_21
DO  - 10.2991/978-2-38476-408-2_21
ID  - Benfarha2025
ER  -