Use of artificial intelligence image generation to promote self-reflection and recognition of unconscious bias: A cross-sectional study of nursing students

IF 4 3区医学 Q1 NURSING

Nurse Education in Practice Pub Date : 2025-10-01 DOI:10.1016/j.nepr.2025.104579

Leanne Mullan, Bethany Arbuckle, Wendy Luck

{"title":"Use of artificial intelligence image generation to promote self-reflection and recognition of unconscious bias: A cross-sectional study of nursing students","authors":"Leanne Mullan, Bethany Arbuckle, Wendy Luck","doi":"10.1016/j.nepr.2025.104579","DOIUrl":null,"url":null,"abstract":"<div><h3>Aim</h3><div>To determine the value of an artificial intelligence (AI)-image generation learning sequence on higher-education nursing student self-reflection and recognition of unconscious bias in the context of disability.</div></div><div><h3>Background</h3><div>Self-reflection and recognition of bias amongst undergraduate nursing students enhances reasoning skills and self-awareness in clinical situations. Teaching self-reflection to a diverse cohort can be challenging, making it essential to develop and assess innovative technological tools that support engagement in reflective practice.</div></div><div><h3>Design</h3><div>A multi-methods approach was adopted, obtaining both quantitative and qualitative data for analysis through a survey.</div></div><div><h3>Methods</h3><div>Twenty-nine nursing students from the Australian Catholic University were surveyed. Qualitative data underwent both content and inductive thematic analysis. Quantitative data were summarised using descriptive statistics. The study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) cross-sectional study guideline.</div></div><div><h3>Results</h3><div>AI-image generation aided self-reflection on personal views about disability and recognition of potential personal and society biases towards disability amongst 90 % (n = 26) and 70 % of participants respectively. Visualisation of thoughts supported self-reflection and identification of generalisations held about disability. Eighty percent of respondents felt AI-image generation prompted them to consider how views and biases about disability may influence nursing practice. AI-image generation was identified to be an interesting and novel tool for self-reflection.</div></div><div><h3>Conclusion</h3><div>Findings suggest AI-image generation may be a useful tool in supporting students to practice self-reflection and identify unconscious biases. AI-image generation may assist students to consider how personal views can impact on clinical practice.</div></div>","PeriodicalId":48715,"journal":{"name":"Nurse Education in Practice","volume":"88 ","pages":"Article 104579"},"PeriodicalIF":4.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nurse Education in Practice","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1471595325003361","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

Aim

To determine the value of an artificial intelligence (AI)-image generation learning sequence on higher-education nursing student self-reflection and recognition of unconscious bias in the context of disability.

Background

Self-reflection and recognition of bias amongst undergraduate nursing students enhances reasoning skills and self-awareness in clinical situations. Teaching self-reflection to a diverse cohort can be challenging, making it essential to develop and assess innovative technological tools that support engagement in reflective practice.

Design

A multi-methods approach was adopted, obtaining both quantitative and qualitative data for analysis through a survey.

Methods

Twenty-nine nursing students from the Australian Catholic University were surveyed. Qualitative data underwent both content and inductive thematic analysis. Quantitative data were summarised using descriptive statistics. The study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) cross-sectional study guideline.

Results

AI-image generation aided self-reflection on personal views about disability and recognition of potential personal and society biases towards disability amongst 90 % (n = 26) and 70 % of participants respectively. Visualisation of thoughts supported self-reflection and identification of generalisations held about disability. Eighty percent of respondents felt AI-image generation prompted them to consider how views and biases about disability may influence nursing practice. AI-image generation was identified to be an interesting and novel tool for self-reflection.

Conclusion

Findings suggest AI-image generation may be a useful tool in supporting students to practice self-reflection and identify unconscious biases. AI-image generation may assist students to consider how personal views can impact on clinical practice.

查看原文本刊更多论文

利用人工智能图像生成促进自我反思和无意识偏见的识别：护理学生的横断面研究

目的探讨人工智能（AI）图像生成学习序列对高教护生残障背景下自我反思和无意识偏见识别的价值。背景护理本科学生的自我反思和偏见认知提高了临床情境中的推理能力和自我意识。向不同群体教授自我反思可能具有挑战性，因此开发和评估支持参与反思实践的创新技术工具至关重要。设计采用多方法方法，通过调查获得定量和定性数据进行分析。方法对澳大利亚天主教大学护理专业学生29名进行问卷调查。定性数据进行了内容分析和归纳性专题分析。定量数据采用描述性统计进行汇总。该研究是根据加强流行病学观察性研究报告（STROBE）横断面研究指南报道的。结果人工智能图像生成分别帮助90% % （n = 26）和70% %的参与者反思个人对残疾的看法，并认识到潜在的个人和社会对残疾的偏见。思想的可视化支持自我反思和对残疾的概括的识别。80%的受访者认为，人工智能图像的生成促使他们考虑有关残疾的观点和偏见可能如何影响护理实践。人工智能图像生成被认为是一种有趣而新颖的自我反思工具。研究结果表明，人工智能图像生成可能是支持学生练习自我反思和识别无意识偏见的有用工具。人工智能图像生成可以帮助学生考虑个人观点如何影响临床实践。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Nurse Education in Practice NURSING-

CiteScore

5.40

自引率

9.40%

发文量

180

审稿时长

51 days

期刊介绍： Nurse Education in Practice enables lecturers and practitioners to both share and disseminate evidence that demonstrates the actual practice of education as it is experienced in the realities of their respective work environments. It is supportive of new authors and will be at the forefront in publishing individual and collaborative papers that demonstrate the link between education and practice.