{"title":"GPT-4 在中国护理考试中的表现：使用大语言模型进行人工智能辅助护理教育的潜力。","authors":"Yiqun Miao, Yuan Luo, Yuhan Zhao, Jiawei Li, Mingxuan Liu, Huiying Wang, Yuling Chen, Ying Wu","doi":"10.1097/NNE.0000000000001679","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The performance of GPT-4 in nursing examinations within the Chinese context has not yet been thoroughly evaluated.</p><p><strong>Objective: </strong>To assess the performance of GPT-4 on multiple-choice and open-ended questions derived from nursing examinations in the Chinese context.</p><p><strong>Methods: </strong>The data sets of the Chinese National Nursing Licensure Examination spanning 2021 to 2023 were used to evaluate the accuracy of GPT-4 in multiple-choice questions. The performance of GPT-4 on open-ended questions was examined using 18 case-based questions.</p><p><strong>Results: </strong>For multiple-choice questions, GPT-4 achieved an accuracy of 71.0% (511/720). For open-ended questions, the responses were evaluated for cosine similarity, logical consistency, and information quality, all of which were found to be at a moderate level.</p><p><strong>Conclusion: </strong>GPT-4 performed well at addressing queries on basic knowledge. However, it has notable limitations in answering open-ended questions. Nursing educators should weigh the benefits and challenges of GPT-4 for integration into nursing education.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance of GPT-4 on Chinese Nursing Examination: Potentials for AI-Assisted Nursing Education Using Large Language Models.\",\"authors\":\"Yiqun Miao, Yuan Luo, Yuhan Zhao, Jiawei Li, Mingxuan Liu, Huiying Wang, Yuling Chen, Ying Wu\",\"doi\":\"10.1097/NNE.0000000000001679\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The performance of GPT-4 in nursing examinations within the Chinese context has not yet been thoroughly evaluated.</p><p><strong>Objective: </strong>To assess the performance of GPT-4 on multiple-choice and open-ended questions derived from nursing examinations in the Chinese context.</p><p><strong>Methods: </strong>The data sets of the Chinese National Nursing Licensure Examination spanning 2021 to 2023 were used to evaluate the accuracy of GPT-4 in multiple-choice questions. The performance of GPT-4 on open-ended questions was examined using 18 case-based questions.</p><p><strong>Results: </strong>For multiple-choice questions, GPT-4 achieved an accuracy of 71.0% (511/720). For open-ended questions, the responses were evaluated for cosine similarity, logical consistency, and information quality, all of which were found to be at a moderate level.</p><p><strong>Conclusion: </strong>GPT-4 performed well at addressing queries on basic knowledge. However, it has notable limitations in answering open-ended questions. Nursing educators should weigh the benefits and challenges of GPT-4 for integration into nursing education.</p>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/NNE.0000000000001679\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/5 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/NNE.0000000000001679","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/5 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Performance of GPT-4 on Chinese Nursing Examination: Potentials for AI-Assisted Nursing Education Using Large Language Models.
Background: The performance of GPT-4 in nursing examinations within the Chinese context has not yet been thoroughly evaluated.
Objective: To assess the performance of GPT-4 on multiple-choice and open-ended questions derived from nursing examinations in the Chinese context.
Methods: The data sets of the Chinese National Nursing Licensure Examination spanning 2021 to 2023 were used to evaluate the accuracy of GPT-4 in multiple-choice questions. The performance of GPT-4 on open-ended questions was examined using 18 case-based questions.
Results: For multiple-choice questions, GPT-4 achieved an accuracy of 71.0% (511/720). For open-ended questions, the responses were evaluated for cosine similarity, logical consistency, and information quality, all of which were found to be at a moderate level.
Conclusion: GPT-4 performed well at addressing queries on basic knowledge. However, it has notable limitations in answering open-ended questions. Nursing educators should weigh the benefits and challenges of GPT-4 for integration into nursing education.
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