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

arXiv:2503.01859 · cs.CL, cs.AI, cs.IR · Submitted 2025-02-23 · Read on arXiv

cs.CL, cs.AI, cs.IR

Submitted: 2025-02-23

Updated: 2025-02-23

Journal ref: Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025), Vol. 2, pp. 174-186, 2025

DOI: 10.5220/0013477700003932

License: http://creativecommons.org/licenses/by/4.0/

The gist: Advances in Large Language Models revolutionized medical education by enabling scalable and efficient learning solutions.

Terminology

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.

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