目的:采用文献计量学方法探讨人工智能在护士教育中的研究热点和主题趋势。
方法:文献计量分析。
方法:检索了从建设之时到2023年10月31日的WebofScience核心合集的文献。
方法:国家分析,作者,机构,期刊,和关键词是使用Bibliometrix(基于R语言)进行的,CiteSpace,在线分析平台(文献计量),Vosviewer,还有Pajek.
结果:共检索了135篇在过去三年中呈直线上升趋势的文章。通过拟合曲线R2=0.6022(R2>0.4),我们预测,未来几年每年的文章数量预计会增加。美国(n=38)新加坡国立大学(n=16),大田君教授(n=8),今天的护士教育(n=14)是国家,机构,作者,以及为大多数出版物做出贡献的期刊,分别。合作网络分析显示,建立了32个机构和64个作者合作团队。我们确定了十个高频关键词和九个聚类。我们将人工智能在护士教育中的研究热点分为三个方面:(1)人工智能增强型仿真机器人,(2)机器学习和数据挖掘,(3)基于自然语言处理和深度学习的大型语言模型。通过分析关键词的时空演变和突发检测,我们发现,未来的研究趋势可能包括(1)扩大和深化人工智能技术的应用,(2)行为意图和教育结果的评估,和(3)道德和伦理考虑。
结论:未来的研究应该在技术应用上进行,行为意图,伦理政策,国际合作,跨学科合作,和可持续性,促进AI在护士教育中的持续发展和创新。
OBJECTIVE: To explore research hotspots and theme trends in artificial intelligence in nurse education using bibliometric analysis.
METHODS: Bibliometric analysis.
METHODS: Literature from the Web of Science Core Collection from the time of construction to October 31, 2023 was searched.
METHODS: Analyses of countries, authors, institutions, journals, and keywords were conducted using Bibliometrix (based on R language), CiteSpace, the online analysis platform (bibliometric), Vosviewer, and Pajek.
RESULTS: A total of 135 articles with a straight upward trend over the last three years were retrieved. By fitting the curve R2 = 0.6022 (R2 > 0.4), we predicted that the number of annual articles is projected to grow in the coming years. The United States (n = 38), the National University of Singapore (n = 16), Professor Jun Ota (n = 8), and Nurse Education Today (n = 14) are the countries, institutions, authors, and journals that contributed to the most publications, respectively. Collaborative network analysis revealed that 32 institutional and 64 author collaborative teams were established. We identified ten high-frequency keywords and nine clusters. We categorized the research hotspots of artificial intelligence in nurse education into three areas: (1) Artificial intelligence-enhanced simulation robots, (2) machine learning and data mining, and (3) large language models based on natural language processing and deep learning. By analyzing the temporal and spatial evolution of keywords and burst detection, we found that future research trends may include (1) expanding and deepening the application of AI technology, (2) assessment of behavioral intent and educational outcomes, and (3) moral and ethical considerations.
CONCLUSIONS: Future research should be conducted on technology applications, behavioral intent, ethical policy, international cooperation, interdisciplinary cooperation, and sustainability to promote the continued development and innovation of AI in nurse education.