{"title":"人工智能在护理教育中的应用和指导的普及:一项关于美国护理资格预审项目的全国性研究","authors":"Brendan Martin PhD , Michaela Reid BS","doi":"10.1016/j.jnr.2025.08.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>There is ample evidence that the integration of artificial intelligence (AI) tools into nursing practice is becoming more commonplace, but there are fewer national resources indicating to what degree prelicensure nursing programs employ these technologies and incorporate related topics into their curriculum.</div></div><div><h3>Purpose</h3><div>The current survey study sought to determine the prevalence of registered nurse (RN) and licensed practical nurse (LPN) education programs’ use of generative AI technologies, and the extent to which they embed AI and other digital health topics into their instructional content.</div></div><div><h3>Methods</h3><div>A national survey was conducted of all RN and LPN program administrators nationwide for which we had email contact information (<em>N</em> = 2744).</div></div><div><h3>Results</h3><div>Prelicensure RN programs (<em>n</em> = 122, 24 %) were more likely to use generative AI technology than LPN programs (<em>n</em> = 27, 12 %, <em>p</em> < 0.001), but more than three-quarters of both types of programs reported they do not use such tools or are not sure. In addition to the low usage of generative AI technology, few programs reported teaching advancements in AI and/or other digital health–related topics to their students (RN <em>n</em> = 87, 17 %; LPN <em>n</em> = 25, 11 %).</div></div><div><h3>Conclusion</h3><div>Nursing education programs that limit integration of AI into their curriculum risk potentially limiting students’ learning on evidence-based practice and may miss opportunities to promote critical reflection. The results of our study underscore the need to support nursing faculty to ensure prelicensure instructional content prepares nursing students for advancements in clinical practice.</div></div>","PeriodicalId":46153,"journal":{"name":"Journal of Nursing Regulation","volume":"16 3","pages":"Pages 216-222"},"PeriodicalIF":6.3000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prevalence of artificial intelligence use and instruction in nursing education: A national study of prelicensure nursing programs in the United States\",\"authors\":\"Brendan Martin PhD , Michaela Reid BS\",\"doi\":\"10.1016/j.jnr.2025.08.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>There is ample evidence that the integration of artificial intelligence (AI) tools into nursing practice is becoming more commonplace, but there are fewer national resources indicating to what degree prelicensure nursing programs employ these technologies and incorporate related topics into their curriculum.</div></div><div><h3>Purpose</h3><div>The current survey study sought to determine the prevalence of registered nurse (RN) and licensed practical nurse (LPN) education programs’ use of generative AI technologies, and the extent to which they embed AI and other digital health topics into their instructional content.</div></div><div><h3>Methods</h3><div>A national survey was conducted of all RN and LPN program administrators nationwide for which we had email contact information (<em>N</em> = 2744).</div></div><div><h3>Results</h3><div>Prelicensure RN programs (<em>n</em> = 122, 24 %) were more likely to use generative AI technology than LPN programs (<em>n</em> = 27, 12 %, <em>p</em> < 0.001), but more than three-quarters of both types of programs reported they do not use such tools or are not sure. In addition to the low usage of generative AI technology, few programs reported teaching advancements in AI and/or other digital health–related topics to their students (RN <em>n</em> = 87, 17 %; LPN <em>n</em> = 25, 11 %).</div></div><div><h3>Conclusion</h3><div>Nursing education programs that limit integration of AI into their curriculum risk potentially limiting students’ learning on evidence-based practice and may miss opportunities to promote critical reflection. The results of our study underscore the need to support nursing faculty to ensure prelicensure instructional content prepares nursing students for advancements in clinical practice.</div></div>\",\"PeriodicalId\":46153,\"journal\":{\"name\":\"Journal of Nursing Regulation\",\"volume\":\"16 3\",\"pages\":\"Pages 216-222\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Nursing Regulation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2155825625000924\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nursing Regulation","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2155825625000924","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
引用次数: 0
摘要
有充分的证据表明,将人工智能(AI)工具整合到护理实践中正变得越来越普遍,但很少有国家资源表明,护理专业在多大程度上采用了这些技术,并将相关主题纳入了他们的课程。当前的调查研究旨在确定注册护士(RN)和执业护士(LPN)教育项目使用生成式人工智能技术的流行程度,以及他们在教学内容中嵌入人工智能和其他数字健康主题的程度。方法对全国所有有电子邮件联系方式的注册护士和LPN项目管理人员(N = 2744)进行调查。结果sprelicensure RN项目(n = 122,24 %)比LPN项目(n = 27,12 %, p < 0.001)更有可能使用生成式人工智能技术,但两种类型的项目中超过四分之三的人报告说他们没有使用这些工具或不确定。除了生成式人工智能技术的使用率较低外,很少有项目向学生报告人工智能和/或其他数字健康相关主题的教学进展(RN n = 87,17%; LPN n = 25,11%)。结论:限制人工智能融入课程的护理教育项目可能会限制学生对循证实践的学习,并可能失去促进批判性反思的机会。我们的研究结果强调需要支持护理教师,以确保执照前的教学内容准备护理学生在临床实践中的进步。
Prevalence of artificial intelligence use and instruction in nursing education: A national study of prelicensure nursing programs in the United States
Background
There is ample evidence that the integration of artificial intelligence (AI) tools into nursing practice is becoming more commonplace, but there are fewer national resources indicating to what degree prelicensure nursing programs employ these technologies and incorporate related topics into their curriculum.
Purpose
The current survey study sought to determine the prevalence of registered nurse (RN) and licensed practical nurse (LPN) education programs’ use of generative AI technologies, and the extent to which they embed AI and other digital health topics into their instructional content.
Methods
A national survey was conducted of all RN and LPN program administrators nationwide for which we had email contact information (N = 2744).
Results
Prelicensure RN programs (n = 122, 24 %) were more likely to use generative AI technology than LPN programs (n = 27, 12 %, p < 0.001), but more than three-quarters of both types of programs reported they do not use such tools or are not sure. In addition to the low usage of generative AI technology, few programs reported teaching advancements in AI and/or other digital health–related topics to their students (RN n = 87, 17 %; LPN n = 25, 11 %).
Conclusion
Nursing education programs that limit integration of AI into their curriculum risk potentially limiting students’ learning on evidence-based practice and may miss opportunities to promote critical reflection. The results of our study underscore the need to support nursing faculty to ensure prelicensure instructional content prepares nursing students for advancements in clinical practice.
期刊介绍:
Journal of Nursing Regulation (JNR), the official journal of the National Council of State Boards of Nursing (NCSBN®), is a quarterly, peer-reviewed, academic and professional journal. It publishes scholarly articles that advance the science of nursing regulation, promote the mission and vision of NCSBN, and enhance communication and collaboration among nurse regulators, educators, practitioners, and the scientific community. The journal supports evidence-based regulation, addresses issues related to patient safety, and highlights current nursing regulatory issues, programs, and projects in both the United States and the international community. In publishing JNR, NCSBN''s goal is to develop and share knowledge related to nursing and other healthcare regulation across continents and to promote a greater awareness of regulatory issues among all nurses.