Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning

Jeremi I. Kaczmarek, Jeremi I. Kaczmarek, Jeremi I. Kaczmarek, Jakub Pokrywka, Jakub Pokrywka, Krzysztof Biedalak, Grzegorz Kurzyp, Łukasz Grzybowski, Łukasz Grzybowski

2025

Abstract

Advances in Large Language Models revolutionized medical education by enabling scalable and efficient learning solutions. This paper presents a pipeline employing Retrieval-Augmented Generation (RAG) system to prepare comments generation for Poland’s State Specialization Examination (PES) based on verified resources. The system integrates these generated comments and source documents with a spaced repetition learning algorithm to enhance knowledge retention while minimizing cognitive overload. By employing a refined retrieval system, query rephraser, and an advanced reranker, our modified RAG solution promotes accuracy more than efficiency. Rigorous evaluation by medical annotators demonstrates improvements in key metrics such as document relevance, credibility, and logical coherence of generated content, proven by a series of experiments presented in the paper. This study highlights the potential of RAG systems to provide scalable, high-quality, and individualized educational resources, addressing non-English speaking users.

Paper Citation

in Harvard Style

Kaczmarek J., Pokrywka J., Biedalak K., Kurzyp G. and Grzybowski Ł. (2025). Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning. In Proceedings of the 17th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-746-7, SciTePress, pages 174-186. DOI: 10.5220/0013477700003932

in Bibtex Style

@conference{csedu25,
author={Jeremi Kaczmarek and Jakub Pokrywka and Krzysztof Biedalak and Grzegorz Kurzyp and Łukasz Grzybowski},
title={Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning},
booktitle={Proceedings of the 17th International Conference on Computer Supported Education - Volume 2: CSEDU},
year={2025},
pages={174-186},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013477700003932},
isbn={978-989-758-746-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Computer Supported Education - Volume 2: CSEDU
TI - Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning
SN - 978-989-758-746-7
AU - Kaczmarek J.
AU - Pokrywka J.
AU - Biedalak K.
AU - Kurzyp G.
AU - Grzybowski Ł.
PY - 2025
SP - 174
EP - 186
DO - 10.5220/0013477700003932
PB - SciTePress