Architecture for Gradually AI-Teamed Adaptation Rules in Learning Management Systems

Niels Seidel

2025

Abstract

Over the last two decades, there has been a substantial advance in developing adaptive learning environments. However, current adaptive learning environments often face limitations, such as tailoring to specific contexts or courses, relying on limited data sources, and focusing on single adaptation goals (e.g., knowledge level). These systems commonly use a single data mining approach and are often tested in isolated studies, restricting broader applicability. Integration with mainstream Learning Management Systems (LMS) also remains challenging, affecting accessibility and scalability in education systems. In this paper, we present a system architecture for authoring and executing adaptation rules to support adaptive learning within Moodle, a widely used LMS that focuses on enhancing self-regulated learning. Using AI methods like rule mining, clustering, reinforcement learning, and large language models can address some of the known disadvantages of rule-based systems. In addition, the support of the adaptation rules can be quantified and simulated using weekly user models from previous semesters. Leveraging an active distance learning course, our investigation reveals an AI-teamed process for identifying, defining, and validating adaptation rules, ensuring the harmonized execution of personalized SRL feedback.

Paper Citation

in Harvard Style

Seidel N. (2025). Architecture for Gradually AI-Teamed Adaptation Rules in Learning Management Systems. In Proceedings of the 17th International Conference on Computer Supported Education - Volume 1: CSEDU; ISBN 978-989-758-746-7, SciTePress, pages 243-254. DOI: 10.5220/0013215800003932

in Bibtex Style

@conference{csedu25,
author={Niels Seidel},
title={Architecture for Gradually AI-Teamed Adaptation Rules in Learning Management Systems},
booktitle={Proceedings of the 17th International Conference on Computer Supported Education - Volume 1: CSEDU},
year={2025},
pages={243-254},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013215800003932},
isbn={978-989-758-746-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Computer Supported Education - Volume 1: CSEDU
TI - Architecture for Gradually AI-Teamed Adaptation Rules in Learning Management Systems
SN - 978-989-758-746-7
AU - Seidel N.
PY - 2025
SP - 243
EP - 254
DO - 10.5220/0013215800003932
PB - SciTePress