{"title":"Teaching Nursing Students Effective Artificial Intelligence Prompt Engineering: The CARE Framework.","authors":"Mary E Bester, Kathryn Zeigler","doi":"10.1097/NNE.0000000000001969","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Effective artificial intelligence (AI) prompting is essential for students to use AI to enhance critical thinking and clinical decision-making skills.</p><p><strong>Methods: </strong>A framework for effective prompting, the CARE (Context, Action, Role, Expectation) Prompt Engineering Framework, was developed. This framework emphasizes the importance of maintaining the \"human connection\" in AI.</p><p><strong>Results: </strong>When students use AI, they use their skills to ask AI for information. Instructors should model responsible and effective AI-prompt engineering. Each CARE element is discussed with remedial prompting to ensure more effective output. AI outputs are verified and reviewed; the original context is revised with the desired changes; and follow-up actions are submitted.</p><p><strong>Conclusions: </strong>The CARE framework provides a systematic outline for nurse educators to use in teaching students clinical decision-making skills, while also capturing the role-modeling behavior of faculty members to ensure that effective AI prompts are used.</p>","PeriodicalId":54706,"journal":{"name":"Nurse Educator","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nurse Educator","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/NNE.0000000000001969","RegionNum":3,"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
Abstract
Background: Effective artificial intelligence (AI) prompting is essential for students to use AI to enhance critical thinking and clinical decision-making skills.
Methods: A framework for effective prompting, the CARE (Context, Action, Role, Expectation) Prompt Engineering Framework, was developed. This framework emphasizes the importance of maintaining the "human connection" in AI.
Results: When students use AI, they use their skills to ask AI for information. Instructors should model responsible and effective AI-prompt engineering. Each CARE element is discussed with remedial prompting to ensure more effective output. AI outputs are verified and reviewed; the original context is revised with the desired changes; and follow-up actions are submitted.
Conclusions: The CARE framework provides a systematic outline for nurse educators to use in teaching students clinical decision-making skills, while also capturing the role-modeling behavior of faculty members to ensure that effective AI prompts are used.
期刊介绍：
Nurse Educator, a scholarly, peer reviewed journal for faculty and administrators in schools of nursing and nurse educators in other settings, provides practical information and research related to nursing education. Topics include program, curriculum, course, and faculty development; teaching and learning in nursing; technology in nursing education; simulation; clinical teaching and evaluation; testing and measurement; trends and issues; and research in nursing education.
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