{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T21:20:50Z","timestamp":1788211250174,"version":"build-2803163510"},"reference-count":19,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Eur J Dental Education"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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\u2010file JavaScript Object Notation (JSON)\u2013based architecture over this conventional approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      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\u2010specific JSON files). We measured API processing time and token counts for generating questions (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20095 trials per condition) and the data were analysed using a two\u2010way analysis of variance (ANOVA).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The modified JSON\u2010based architecture demonstrated superior efficiency. For a 10\u2010question task, it significantly reduced API processing time (97.0 vs. 115.6\u2009s,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and reduced input token consumption by approximately 88% (95\u2009197 vs. 813\u2009702 tokens,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) compared with the unmodified method. All generated question sets passed automated structural and formatting validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In conclusion, a structured, multi\u2010file JSON architecture dramatically enhances the efficiency and scalability of LLM\u2010based 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/eje.70289","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T20:44:44Z","timestamp":1788209084000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimising Large Language Models\u2010Based Automated Question Generation for Dental Pharmacology: A Quantitative Evaluation of a Multi\u2010File\n                    <scp>JavaScript<\/scp>\n                    Object Notation Architecture"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6068-6268","authenticated-orcid":false,"given":"Fumihiko","family":"Yoshino","sequence":"first","affiliation":[{"name":"Department of Pharmacology Kanagawa Dental University  Yokosuka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8855-9228","authenticated-orcid":false,"given":"Ayaka","family":"Yoshida","sequence":"additional","affiliation":[{"name":"Department of Liberal Arts Education Kanagawa Dental University  Yokosuka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shun\u2010suke","family":"Takahashi","sequence":"additional","affiliation":[{"name":"Department of Pharmacology Kanagawa Dental University  Yokosuka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1483-2700","authenticated-orcid":false,"given":"Tomoki","family":"Itamiya","sequence":"additional","affiliation":[{"name":"Department of Liberal Arts Education Kanagawa Dental University  Yokosuka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3981-9323","authenticated-orcid":false,"given":"Satoko","family":"Wada\u2010Takahashi","sequence":"additional","affiliation":[{"name":"Department of Physiology Kanagawa Dental University  Yokosuka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akito","family":"Nakano","sequence":"additional","affiliation":[{"name":"Department of Liberal Arts Education Kanagawa Dental University  Yokosuka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adaj.2015.01.003"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41999\u2010021\u201000481\u20109"},{"key":"e_1_2_11_4_1","unstructured":"Ministry of Education Culture Sports Science and Technology \u201cModel Core Curriculum for Dental Education in Japan AY 2022 Revision \u201d(2022)."},{"key":"e_1_2_11_5_1","unstructured":"Ministry of Health Labour and Welfare \u201cShika\u2010Ishi Kokka Shiken Shutsudai Kijun [National Dental Practitioner Examination Guidelines] Reiwa 5 Edition \u201d(2022)."},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12909\u2010024\u201005239\u2010y"},{"key":"e_1_2_11_7_1","article-title":"FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance","author":"Chen L.","year":"2024","journal-title":"Transactions on Machine Learning Research"},{"key":"e_1_2_11_8_1","first-page":"157","article-title":"Comparison of JSON and XML Data Interchange Formats: A Case Study","volume":"9","author":"Nurseitov N.","year":"2009","journal-title":"Proceedings of the International Conference on Computer Applications in Industry and Engineering"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e28198"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3420709"},{"issue":"1189","key":"e_1_2_11_11_1","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1093\/postmj\/qgae065","article-title":"ChatGPT Prompts for Generating Multiple\u2010Choice Questions in Medical Education and Evidence on Their Validity: A Literature Review","volume":"100","author":"K\u0131yak Y. 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