Personalized Language Learning: A Multi-Agent System Leveraging LLMs for Teaching Luxembourgish

Authors

Hedi T., Nouzri S., Mualla Y., Najjar A.

Reference

Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas, pp. 3032-3034, 2025

Description

The integration of Artificial Intelligence (AI) into education is transforming language learning. Current chatbot-based tools primarily focus on vocabulary acquisition and conversation, overlooking the holistic needs of effective language learning, such as grammar, reading, and listening skills. These limitations are further compounded by the challenges of low-resource languages like Luxembourgish. This demonstration¹ presents a Multi-Agent System (MAS) powered by Large Language Models (LLMs), integrated with Retrieval-Augmented Generation (RAG) to address these challenges. Our system personalizes learning by employing specialized agents for specialized tasks, ensuring a comprehensive and adaptive experience. To mitigate inaccuracies, human-on-the-loop (here teacher) validation enhances content quality and aligns with pedagogical standards inspired by the National Institute of Languages of Luxembourg (INL). Attendees will experience interactive demonstrations showcasing how the system delivers tailored educational experiences through innovative agent workflows and user-centric design.