Design and Implementation of an Adaptive Tutoring System for Enhanced E-Learning
DOI:
https://doi.org/10.56294/dm2025469Keywords:
E-learning, Intelligent Tutoring Systems, Personalized E-learning, Adaptive E-learning, Digital Transformation, Adaptive Tutoring Systems, ATS, Data Analytics, User ExperienceAbstract
The increasing offer of new information and communication technologies has changed the educational field, e-learning emerged as an important complement to traditional face-to-face education and often a good alternative in many contexts. This shift has been emphasized by global challenges such as the COVID-19 pandemic, which highlighted the importance of remote learning platforms and their effectiveness in such situations. However, many challenges such as the costs and the need for personalized and interactive learning environments remain an obstacle. To address these issues, adaptive e-learning systems and Intelligent Tutoring Systems (ITS) are increasingly being developed and given support by education communities and governments. These systems aim to adapt content to the learner’s cognitive abilities and individual learning styles, for better understanding and retention. This paper explores the design and development of an adaptive ITS, which integrates Artificial Intelligence and data analytics to provide better learning experience. This paper puts the light on the role of adaptive hypermedia in educational interactions, analyzing its key features and how they can be leveraged to enhance learning outcomes. By incorporating learning success metrics, our study provides a comprehensive perspective on the potential of ITS to revolutionize adaptive and personalized e-learning systems, driving significant improvements in both learner engagement and achievement.
References
[1] Michalas, A., & Vretaros, P. (2023). Protecting medical emergency personnel through a framework for verifiable and privacy-preserving cross-border collaboration. ICT Systems Security and Privacy Protection, 82-94. https://doi.org/10.1007/978-3-031-54288-6_5
[2] Shraim, K. (2024). Artificial Intelligence (AI)-Powered Tutoring Systems: Exploring Their Potential to Reduce Dropout Rates in E-Learning. In A. K. M. Azad & M. K. Hasan (Eds.), Artificial Intelligence in Education: Toward a Better Future (pp. 255–275). Springer. https://doi.org/10.1007/978-3-031-54288-6_15
[3] Kulkarni, S., & Ogata, H. (2020). Intelligent Tutoring Systems: Opportunities and Challenges in E-Learning. Educational Technology & Society, 23(3), 34-45.
[4] Smith, A., & Johnson, B. (2021). The Impact of Intelligent Tutoring Systems on Student Learning. Journal of Educational Technology Research, 15(2), 123-145.
[5] Brusilovsky, P., & Millán, E. (2007). User Models for Adaptive Hypermedia and Adaptive Educational Systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web: Methods and Strategies of Web Personalization (pp. 3-53). Springer.
[6] Shute, V. J., & Zapata-Rivera, D. (2012). Adaptive Educational Systems. In P. J. Durlach & A. M. Lesgold (Eds.), Adaptive Technologies for Training and Education (pp. 7-27). Cambridge University Press.
[7] De Jong, T., & van Joolingen, W. R. (1998). Scientific Discovery Learning with Computer Simulations of Conceptual Domains. Review of Educational Research, 68(2), 179-201.
[8] Baker, R. S. J. d., & Inventado, P. S. (2014). Educational Data Mining and Learning Analytics. In Learning Analytics (pp. 61-75). Springer.
[9] Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education.
[10] Pane, J. F., Steiner, E. D., Baird, M. D., Hamilton, L. S., & Pane, J. D. (2017). Informing Progress: Insights on Personalized Learning Implementation and Effects. RAND Corporation.
[11] Cloude, E. B., Carpenter, D. D., Choe, E. K., & Lee, D. (2022). Multimodal Learning Analytics to Quantify Student Behaviors in Complex Digital Learning Environments. Computers & Education, 176, 104352.
[12] Stamper, J., & Koedinger, K. R. (2022). Enhancing LLM-Based Feedback: Insights from Intelligent Tutoring Systems and Learning Sciences. arXiv.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Atmane EL HADBI , Mohammed Hatim RZIKI , Yassine JAMIL , Zaynab AMMARI , Mohamed Khalifa BOUTAHIR , Hamid Bourray, Driss EL OUADGHIRI (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
