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Learner Models: Requirements and Legal Issues for the Development and Application of Learner Models

Authors:
Felix Böck
Hendrik Link
Dieter Landes

Keywords: learner model; learner modelling; requirements engineering; legal issues; compliance; ethical principles; higher education; learning analytics

Abstract:
Learners differ vastly in various aspects of what they need for successful learning. Artificial Intelligence (AI) establishes a basis for digital learning environments which adapt themselves automatically to the learners’ needs. To be able to do so, these systems presuppose knowledge on the individual learner. Learner models are digital representations of learner characteristics that aim to enable personalised and adaptive learning experiences, touching upon issues in key areas, such as transparency, fairness, data protection, modularity, and sustainability. Such learner models form the core of AI-based adaptive learning environments, as they store data about individual learners. This paper collects and discusses requirements, legal issues, and challenges associated with developing and using learner models, particularly in the context of European regulations. By reviewing existing standards, scientific publications, and practical use cases, we identify gaps in standardisation and propose foundational requirements for the design of interoperable and legally compliant learner models. Our findings lay the groundwork for developing a reference architecture, facilitating scalable and ethical integration of learner models in digital learning environments.

Pages: 27 to 35

Copyright: Copyright (c) IARIA, 2025

Publication date: September 28, 2025

Published in: conference

ISBN: 978-1-68558-303-3

Location: Lisbon, Portugal

Dates: from September 28, 2025 to October 2, 2025