{"title":"人工智能(AI)促进汇报:一项试点研究","authors":"Laura Gonzalez PhD , Arjun Nagendran PhD","doi":"10.1016/j.ecns.2025.101782","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Debriefing is a cornerstone of simulation-based education (SBE), enabling reflective practice to enhance learning outcomes. Artificial intelligence (AI)-facilitated debriefing is an emerging innovation with limited research in nursing education.</div></div><div><h3>Purpose</h3><div>This study explored the relationship between time spent in an AI-facilitated debrief, the number of reflective dimensions met, and student performance in a virtual nursing simulation.</div></div><div><h3>Methods</h3><div>A mixed-methods approach integrated quantitative data (simulation scores, time in debrief, number of dimensions met) and qualitative insights from AI algorithm. Participants (<em>N</em> = 52) completed a screen-based simulation and an AI-facilitated debrief guided by the EMPOWER® Debriefing Framework. Descriptive statistics, Pearson’s correlations, and t-tests were conducted; qualitative data underwent thematic analysis.</div></div><div><h3>Results</h3><div>No significant correlation was found between time in debrief and grades (<em>r</em> = 0.07, <em>p</em> = .46). Students meeting more dimensions (3-4) spent significantly less time in debrief (<em>M</em> = 9.39 min) than those meeting fewer dimensions (1–2) (<em>M</em> = 12.62 min), t(50)=5.43, <em>p</em> < .001.</div></div><div><h3>Conclusions</h3><div>AI-facilitated debriefing shows potential for scalable reflective practice but may not replace the depth of human-facilitated sessions. Integration into nursing education requires further validation of reflective outcomes.</div></div>","PeriodicalId":48753,"journal":{"name":"Clinical Simulation in Nursing","volume":"105 ","pages":"Article 101782"},"PeriodicalIF":2.5000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence (AI)-facilitated debriefing: A pilot study\",\"authors\":\"Laura Gonzalez PhD , Arjun Nagendran PhD\",\"doi\":\"10.1016/j.ecns.2025.101782\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Debriefing is a cornerstone of simulation-based education (SBE), enabling reflective practice to enhance learning outcomes. Artificial intelligence (AI)-facilitated debriefing is an emerging innovation with limited research in nursing education.</div></div><div><h3>Purpose</h3><div>This study explored the relationship between time spent in an AI-facilitated debrief, the number of reflective dimensions met, and student performance in a virtual nursing simulation.</div></div><div><h3>Methods</h3><div>A mixed-methods approach integrated quantitative data (simulation scores, time in debrief, number of dimensions met) and qualitative insights from AI algorithm. Participants (<em>N</em> = 52) completed a screen-based simulation and an AI-facilitated debrief guided by the EMPOWER® Debriefing Framework. Descriptive statistics, Pearson’s correlations, and t-tests were conducted; qualitative data underwent thematic analysis.</div></div><div><h3>Results</h3><div>No significant correlation was found between time in debrief and grades (<em>r</em> = 0.07, <em>p</em> = .46). Students meeting more dimensions (3-4) spent significantly less time in debrief (<em>M</em> = 9.39 min) than those meeting fewer dimensions (1–2) (<em>M</em> = 12.62 min), t(50)=5.43, <em>p</em> < .001.</div></div><div><h3>Conclusions</h3><div>AI-facilitated debriefing shows potential for scalable reflective practice but may not replace the depth of human-facilitated sessions. 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引用次数: 0
摘要
汇报是基于模拟的教育(SBE)的基石,使反思性实践能够提高学习成果。人工智能(AI)促进述职是一项新兴的创新,在护理教育方面的研究有限。目的:本研究探讨了在人工智能辅助汇报中花费的时间、满足的反思维度数量与学生在虚拟护理模拟中的表现之间的关系。方法采用混合方法综合定量数据(模拟得分、汇报时间、满足维度数)和人工智能算法的定性见解。参与者(N = 52)完成了基于屏幕的模拟和由EMPOWER®汇报框架指导的人工智能促进的汇报。进行描述性统计、Pearson相关和t检验;对定性数据进行专题分析。结果评议时间与评分无显著相关(r = 0.07, p = 0.46)。维度较多(3-4)的学生汇报时间(M = 9.39 min)明显少于维度较少(1-2)的学生(M = 12.62 min), t(50)=5.43, p <;措施。人工智能促进的汇报显示了可扩展的反思实践的潜力,但可能无法取代人类促进的会议的深度。纳入护理教育需要进一步验证反思性结果。
Artificial intelligence (AI)-facilitated debriefing: A pilot study
Background
Debriefing is a cornerstone of simulation-based education (SBE), enabling reflective practice to enhance learning outcomes. Artificial intelligence (AI)-facilitated debriefing is an emerging innovation with limited research in nursing education.
Purpose
This study explored the relationship between time spent in an AI-facilitated debrief, the number of reflective dimensions met, and student performance in a virtual nursing simulation.
Methods
A mixed-methods approach integrated quantitative data (simulation scores, time in debrief, number of dimensions met) and qualitative insights from AI algorithm. Participants (N = 52) completed a screen-based simulation and an AI-facilitated debrief guided by the EMPOWER® Debriefing Framework. Descriptive statistics, Pearson’s correlations, and t-tests were conducted; qualitative data underwent thematic analysis.
Results
No significant correlation was found between time in debrief and grades (r = 0.07, p = .46). Students meeting more dimensions (3-4) spent significantly less time in debrief (M = 9.39 min) than those meeting fewer dimensions (1–2) (M = 12.62 min), t(50)=5.43, p < .001.
Conclusions
AI-facilitated debriefing shows potential for scalable reflective practice but may not replace the depth of human-facilitated sessions. Integration into nursing education requires further validation of reflective outcomes.
期刊介绍:
Clinical Simulation in Nursing is an international, peer reviewed journal published online monthly. Clinical Simulation in Nursing is the official journal of the International Nursing Association for Clinical Simulation & Learning (INACSL) and reflects its mission to advance the science of healthcare simulation.
We will review and accept articles from other health provider disciplines, if they are determined to be of interest to our readership. The journal accepts manuscripts meeting one or more of the following criteria:
Research articles and literature reviews (e.g. systematic, scoping, umbrella, integrative, etc.) about simulation
Innovative teaching/learning strategies using simulation
Articles updating guidelines, regulations, and legislative policies that impact simulation
Leadership for simulation
Simulation operations
Clinical and academic uses of simulation.