Optimising Large Language Models-Based Automated Question Generation for Dental Pharmacology: A Quantitative Evaluation of a Multi-File JavaScript Object Notation Architecture.

Authors

Fumihiko Yoshino, Ayaka Yoshida, Shun-Suke Takahashi, Tomoki Itamiya, Satoko Wada-Takahashi, Akito Nakano

Published in

European journal of dental education : official journal of the Association for Dental Education in Europe. Aug 31, 2026. Epub Aug 31, 2026.

Abstract

The application of Large Language Models (LLMs) for automated question generation in dental education is hindered by inefficient data management, where conventional systems using a single data file incur high Application Programming Interface (API) costs and slow performance due to excessive token consumption. This study aimed to quantitatively validate the performance benefits of an optimized, multi-file JavaScript Object Notation (JSON)-based architecture over this conventional approach.
We developed a system using Gemini 2.5 Pro and compared two architectures: a conventional unmodified method (single Markdown file) and our proposed modified method (multiple, purpose-specific JSON files). We measured API processing time and token counts for generating questions (n = 5 trials per condition) and the data were analysed using a two-way analysis of variance (ANOVA).
The modified JSON-based architecture demonstrated superior efficiency. For a 10-question task, it significantly reduced API processing time (97.0 vs. 115.6 s, p < 0.001) and reduced input token consumption by approximately 88% (95 197 vs. 813 702 tokens, p < 0.001) compared with the unmodified method. All generated question sets passed automated structural and formatting validation.
In conclusion, a structured, multi-file JSON architecture dramatically enhances the efficiency and scalability of LLM-based educational tools. This approach provides a practical framework for applying AI in dynamic fields such as pharmacology and may help reduce operational burden in modern curriculum development.

PMID:
42673577
Bibliographic data and abstract were imported from PubMed on 01 Sep 2026.

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