@inproceedings{marciniak-etal-2025-improving,
    title = "Improving {AI} assistants embedded in short e-learning courses with limited textual content",
    author = "Marciniak, Jacek  and
      Kubis, Marek  and
      Gulczy{\'n}ski, Micha{\l}  and
      Szpilkowski, Adam  and
      Wieczarek, Adam  and
      Szczepa{\'n}ski, Marcin",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.bea-1.57/",
    pages = "794--804",
    ISBN = "979-8-89176-270-1",
    abstract = "This paper presents a strategy for improving AI assistants embedded in short e-learning courses. The proposed method is implemented within a Retrieval-Augmented Generation (RAG) architecture and evaluated using several retrieval variants. The results show that query quality improves when the knowledge base is enriched with definitions of key concepts discussed in the course. Our main contribution is a lightweight enhancement approach that increases response quality without overloading the course with additional instructional content."
}