Open Access
| Issue |
E3S Web Conf.
Volume 698, 2026
First International Conference on Research and Advancements in Electronics, Energy, and Environment (ICRAEEE 2025)
|
|
|---|---|---|
| Article Number | 01010 | |
| Number of page(s) | 6 | |
| Section | Electrical and Electronic Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202669801010 | |
| Published online | 16 March 2026 | |
- P. Perrenoud, L’évaluation des élèves: De la fabrication de l’excellence à la régulation des apprentissages. Bruxelles: De Boeck, 1998. [Google Scholar]
- R. F. Kizilcec, M. Pérez-Sanagustín, and J. J. Maldonado, “Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses,” Computers & Education, vol. 104, pp. 18–33, 2017, doi:https://doi.org/10.1016/j.compedu.2016.10.001 [Google Scholar]
- J. Fang, Z. Meng, and C. MacDonald, “KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation,” in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vienna, Austria: Association for Computational Linguistics, 2025,pp.1896918985.doi:https://doi.org/10.18653/v1/2025.acl-long.929. [Google Scholar]
- X. Zhao, “A hybrid deep learning and fuzzy logic framework for feature-based evaluation of english Language learners,” Scientific Reports, vol. 15, no. 1, p. 33657, 2025, doi: https://doi.org/10.1038/s41598-025-17738-z. [Google Scholar]
- O. Henkel, Z. Levonian, C. Li, and M. Postle, “Retrieval-Augmented Generation to Improve Math Question-Answering: Trade-Offs Between Groundedness and Human Preference,” in Proceedings of the 17th International Conference on Educational Data Mining, International Educational Data Mining Society, 2024, pp. 315–320. https://doi.org/10.48550/arXiv.2310.03184. [Google Scholar]
- L. Y. Tan, S. Hu, D. J. Yeo, and K. H. Cheong, “Artificial intelligence-enabled adaptive learning platforms: A review,” Computers and Education: Artificial Intelligence, vol. 9, p. 100429,2025, doi: https://doi.org/10.1016/j.caeai.2025.100429. [Google Scholar]
- S. Wang, F. Wang, Z. Zhu, J. Wang, T. Tran, and Z. Du, “Artificial Intelligence in Education: A Systematic Literature Review,” Expert Systems with Applications, vol. 252, p. 124167, 2024,https://doi.org/10.1016/j.eswa.2024.124167. [Google Scholar]
- O. Boateng and B. Boateng, “Algorithmic Bias in Educational Systems: Examining the Impact of AI-Driven Decision Making in Modern Education,” World Journal of Advanced Research and Reviews, vol. 25, no. 1, pp. 2012–2017,2025,doi:https://doi.org/10.30574/wjarr.2025.25.1.0253. [Google Scholar]
- D. C. Fajardo-Ramos, A. Chiappe, and J. Mella-Norembuena, “Human-in-the-Loop Assessment with AI: Implications for Teacher Education in Ibero-American Universities,” Frontiers in Education,vol.10,2025,doi:https://doi.org/10.3389/feduc.2025.1710992. [Google Scholar]
- Z. M. Altukhi and S. Pradhan, “Systematic Literature Review: Explainable AI Definitions and Challenges in Education,” arXiv preprint, vol.arXiv:2504,2025,doi:https://doi.org/10.48550/arXiv.2504.02910. [Google Scholar]
- Y. R. Marín et al., “Ethical Challenges Associated with the Use of Artificial Intelligence in University Education,” Journal of Academic Ethics, vol. 23, pp. 2443–2467, 2025, doi: https://doi.org/10.1007/s10805-025-09660-w. [Google Scholar]
- Y. Li, Z. Shan, M. Raković, Q. Guan, D. Gašević, and G. Chen, “When AI explains in natural language: Unveiling the impact of generative AI explanations on educators’ grading and feedback practices,” Education and Information Technologies, 2025, doi: https://doi.org/10.1007/s10639-025-13741-z. [Google Scholar]
- E. H. Adnani A., “L’intelligence Artificielle au Maroc: Entre éthique et réglementation,” Revue Internationale de la Recherche Scientifique, 2024,doi:https://doi.org/10.5281/zenodo.1162102. [Google Scholar]
- B. S. Neupane P., Évaluation des besoins en intelligence artificielle en Afrique. 2021. [Google Scholar]
- K. Smahi, O. Labouidya, and K. EL Khadiri, “Enhancing Online Assessment Quality in Higher Education: The Design of Moodle Plug-in for Personalised Exam Revision (PER),” International Journal of Engineering Pedagogy, vol. 15, no. 5, pp. 127–140, 2025, doi: https://doi.org/10.3991/ijep.v15i5.55055. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

