Student Chatbot Marcel (EMNLP’25)
At this year’s EMNLP in Suzhou, China, Jan Trienes presented Marcel—a RAG-based conversational agent built to handle enrollment questions at Marburg University (Trienes et al., 2025). The project grew out of a simple observation: as our M.Sc. Data Science program expanded, so did the repetitive workload for staff answering student inquiries about deadlines, prerequisites, and language requirements. So, we set up a project.
How Marcel Works
- Precision answers: Pulls up-to-date information from university resources (websites, exam regulations) using retrieval-augmented generation.
- Designed for real-world use: A Vue.js frontend and FastAPI backend make it adaptable, while an admin dashboard lets non-technical staff adjust FAQ retrieval.
- Privacy-first: Runs on-premise in containers, with no external dependencies.
Marcel is already in use, but we’re always open to suggestions—or collaborations with other institutions facing similar challenges.
Reference
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Jan Trienes, Anastasiia Derzhanskaia, Roland Schwarzkopf, Markus Mühling, Jörg Schlötterer, and Christin Seifert.
Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support.
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations.
2025.
BibTeX
@inproceedings{Trienes2025_emnlp_marcel-chatbot, author = {Trienes, Jan and Derzhanskaia, Anastasiia and Schwarzkopf, Roland and M{\"u}hling, Markus and Schl{\"o}tterer, J{\"o}rg and Seifert, Christin}, booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, title = {Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support}, year = {2025}, address = {Suzhou, China}, editor = {Habernal, Ivan and Schulam, Peter and Tiedemann, J{\"o}rg}, month = nov, pages = {181--195}, publisher = {Association for Computational Linguistics}, code = {https://github.com/aix-group/marcel-chat}, doi = {10.18653/v1/2025.emnlp-demos.13}, isbn = {979-8-89176-334-0}, url = {https://aclanthology.org/2025.emnlp-demos.13/} }