Content Adaptation based on Machine learning / AI
This involves content personalisation and adaptive learning through artificial intelligence (AI) and machine learning–driven modelling of personal traits and learning needs. A key approach is the automatic modelling of relationships between learning resources and their alignment with individual learner characteristics. For example, machine learning techniques can extract prerequisite relationships between learning objects, enabling systems to dynamically recommend or sequence content based on a learner’s current knowledge or needs. This ensures that learners receive material tailored to their requirements and supports their knowledge progression. Personalisation can be achieved using integrated machine learning models or AI-based tools.
Resources
- **What Is Machine Learning's Role in EdTech Personalization?**
- Generative AI for Personalized Learning Environments
- How can AI enhance personalised English learning?
- How To Make Learning Fun With Personalized AI Education!
- Generative AI for Personalized and Inclusive Learning
- AI Powered Personalized Learning with Michele Klein
- Personalized Machine Learning
- How to create AI-powered personalized learning plans for every student
References
- Farhood, H., Nyden, M., Beheshti, A., & Muller, S. (2025). Artificial intelligence-based personalised learning in education: A systematic literature review. Discover Artificial Intelligence, 5(1), 331. (pdf resource attached)
- Gasparetti, F., De Medio, C., Limongelli, C., Sciarrone, F., & Temperini, M. (2018). Prerequisites between learning objects: Automatic extraction based on a machine learning approach. Telematics and Informatics, 35(3), 595–610. https://doi.org/10.1016/j.tele.2017.05.007
- Guo, X., He, X., & Pei, Z. (2023, November). Data-driven personalized learning. In Proceedings of the 2023 6th International Conference on Educational Technology Management (pp. 49–54).
- Muñoz-Arteaga, J., Enríquez-González, J. C., Menéndez-Domínguez, V. H., & Zapata-González, A. (2023). Personal learning environments for inclusive university studies with STEAM: An architectural approach. In CEUR Workshop Proceedings (Vol. 3691, pp. 333–347). https://www.scopus.com/inward/record.uri?eid=2-s2.0-
- Nacheva-Skopalik, L., & Green, S. (2012). Adaptable personal e-assessment. International Journal of Web-Based Learning and Teaching Technologies, 7(4), 29–39. https://doi.org/10.4018/jwltt.2012100103
- Vorobyeva, K. I., Belous, S., Savchenko, N. V., Smirnova, L. M., Nikitina, S. A., & Zhdanov, S. P. (2025). Personalized learning through AI: Pedagogical approaches and critical insights. Contemporary Educational Technology, 17(2).