AI-Based Personalized Multilingual Course Recommender System Using Large Language Models

Sourav Dutta, Florian Beier, Dirk Werth

2025

Abstract

This paper presents an AI-driven personalized course recommender system designed to enhance user engagement and learning outcomes on educational platforms. Leveraging the EU DigComp competency framework, the system constructs detailed user profiles through a chat assistant that guides users in identifying relevant competency areas and completing tailored surveys. Course recommendations are generated based on a hybrid scoring model that integrates semantic similarity and competency alignment, ensuring that course suggestions are both contextually and skill-relevant. For users seeking structured guidance, the system offers a learning path feature, utilizing a large language model to suggest subsequent courses that align with the user’s interests and prior learning experiences. While traditional course recommenders often rely on simple keyword matching, our system dynamically combines user interests and competencies for nuanced recommendations across English and German courses. Screenshots of the system’s live demo showcase key functionalities, including chatbot-led profile creation, multilingual support, personalized learning paths. This paper highlights the ongoing development of the recommender system and discusses future plans to further refine and expand its personalized learning capabilities.

Paper Citation

in Harvard Style

Dutta S., Beier F. and Werth D. (2025). AI-Based Personalized Multilingual Course Recommender System Using Large Language Models. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1069-1076. DOI: 10.5220/0013260100003890

in Bibtex Style

@conference{icaart25,
author={Sourav Dutta and Florian Beier and Dirk Werth},
title={AI-Based Personalized Multilingual Course Recommender System Using Large Language Models},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1069-1076},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013260100003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - AI-Based Personalized Multilingual Course Recommender System Using Large Language Models
SN - 978-989-758-737-5
AU - Dutta S.
AU - Beier F.
AU - Werth D.
PY - 2025
SP - 1069
EP - 1076
DO - 10.5220/0013260100003890
PB - SciTePress