Learning to Learn via Artificial Intelligence and Learning Analytics
Abstract
This paper addresses the issue of how artificial intelligence and learning analytics are used to help learners improve their self-regulated learning skills, in other words, “learn how to learn”. Two broad approaches are described. The first approach involves artificial intelligence systems that both teach content as well as support self-regulated learning by means of dynamic adjustment to various aspects of the interaction, including dynamic advice and feedback. Such systems are characterised as either purely reactive, explicit or pro-active. The second approach involves learning analytic systems based on artificial intelligence techniques that capture and analyse learning data from various kinds of educational interactions and later feed that back to learners in a form that enables them to visualise and reflect on their learning behaviour. Whilst the first approach is described by reference to the literature, the second approach showcases experimental work conducted by two of the authors. This involved the analysis of audio transcripts of students’ weekly group discussions and the development of different machine learning techniques to capture the challenge moments, triggers of regulation of learning, that would form the basis for feedback implemented in real-world contexts. This informs the future design of the learning analytics and artificial intelligence system to promote awareness and support learners’ subsequent regulation of learning.
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About this article
Publisher
Emanate Publishing House Ltd.
Volume
2
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-53
Subjects
Education, educational psychology, education and technology, education and AI, pedagogy, teacher education
Cite this article as:
Suraworachet, W., Cukurova, M., & du Boulay, B. (2026). Learning to Learn via Artificial Intelligence and Learning Analytics. In Z. Bekiroğulları (Ed.), Proceedings of the 16th International Conference on Education and Educational Psychology (ICEEPSY 2025), vol 2. Emanate - Educational Sessions Highlights (pp. 12-33). Emanate Publishing House Ltd.. https://doi.org/10.70020/eesh.2026.05.2