Authors:
Sourav Dutta
;
Florian Beier
and
Dirk Werth
Affiliation:
August-Wilhelm Scheer Institut gGmbH, Uni-Campus D 5 1, 66123 Saarbrücken, Germany
Keyword(s):
Recommender System, Large Language Model, Natural Language Processing, Generative AI, AI in Education.
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. Screensh
ots 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.
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