{"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. 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":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Simulation in Nursing","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1876139925000994","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
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.