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This paper presents a novel learner profiling methodology developed within a European AI-focused education project. By integrating data from learning platforms, questionnaire responses, and engagement metrics, the study defines interpretable learner profiles using a hybrid pipeline combining unsupervised clustering, supervised classification, and explainable AI. Using case study data from a pilot implementation, the methodology generates meaningful profiles and provides transparent, actionable insights into learner behavior and needs. The paper contributes to the development of personalised and trustworthy AI systems in education.

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Towards Explainable Learner Profiling in AI-Enhanced Education

  • 会议论文

  • 发表日期：

    2026年

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  • N. Alimpertis,
  • G. Domalis,
  • D. Tsakalidis,
  • C. Spyropoulou,
  • M. Anagnostopoulou,
  • E. Tsourlidaki

摘要

This paper presents a novel learner profiling methodology developed within a European AI-focused education project. By integrating data from learning platforms, questionnaire responses, and engagement metrics, the study defines interpretable learner profiles using a hybrid pipeline combining unsupervised clustering, supervised classification, and explainable AI. Using case study data from a pilot implementation, the methodology generates meaningful profiles and provides transparent, actionable insights into learner behavior and needs. The paper contributes to the development of personalised and trustworthy AI systems in education.