Improving AI assistants embedded in short e-learning courses with limited textual content

Jacek Marciniak; Marek Kubis; Michał Gulczyński; Adam Szpilkowski; Adam Wieczarek; Marcin Szczepański

doi:10.18653/v1/2025.bea-1.57

Improving AI assistants embedded in short e-learning courses with limited textual content

Jacek Marciniak, Marek Kubis, Michał Gulczyński, Adam Szpilkowski, Adam Wieczarek, Marcin Szczepański

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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.

Anthology ID:: 2025.bea-1.57
Volume:: Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)
Month:: July
Year:: 2025
Address:: Vienna, Austria
Editors:: Ekaterina Kochmar, Bashar Alhafni, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan
Venues:: BEA | WS
SIG:: SIGEDU
Publisher:: Association for Computational Linguistics
Note:
Pages:: 794–804
Language:
URL:: https://aclanthology.org/2025.bea-1.57/
DOI:: 10.18653/v1/2025.bea-1.57
Bibkey:
Cite (ACL):: Jacek Marciniak, Marek Kubis, Michał Gulczyński, Adam Szpilkowski, Adam Wieczarek, and Marcin Szczepański. 2025. Improving AI assistants embedded in short e-learning courses with limited textual content. In Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025), pages 794–804, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):: Improving AI assistants embedded in short e-learning courses with limited textual content (Marciniak et al., BEA 2025)
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PDF:: https://aclanthology.org/2025.bea-1.57.pdf

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@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://aclanthology.org/2025.bea-1.57/",
    doi = "10.18653/v1/2025.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."
}

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%0 Conference Proceedings
%T Improving AI assistants embedded in short e-learning courses with limited textual content
%A Marciniak, Jacek
%A Kubis, Marek
%A Gulczyński, Michał
%A Szpilkowski, Adam
%A Wieczarek, Adam
%A Szczepański, Marcin
%Y Kochmar, Ekaterina
%Y Alhafni, Bashar
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-270-1
%F marciniak-etal-2025-improving
%X 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.
%R 10.18653/v1/2025.bea-1.57
%U https://aclanthology.org/2025.bea-1.57/
%U https://doi.org/10.18653/v1/2025.bea-1.57
%P 794-804

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Markdown (Informal)

[Improving AI assistants embedded in short e-learning courses with limited textual content](https://aclanthology.org/2025.bea-1.57/) (Marciniak et al., BEA 2025)

Improving AI assistants embedded in short e-learning courses with limited textual content (Marciniak et al., BEA 2025)

ACL

Jacek Marciniak, Marek Kubis, Michał Gulczyński, Adam Szpilkowski, Adam Wieczarek, and Marcin Szczepański. 2025. Improving AI assistants embedded in short e-learning courses with limited textual content. In Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025), pages 794–804, Vienna, Austria. Association for Computational Linguistics.