Hotspots analysis of artificial intelligence in nursing education based on CiteSpace
10.3760/cma.j.cn211501-20240207-00328
- VernacularTitle:基于CiteSpace的人工智能在护理教育领域研究热点可视化分析
- Author:
Ting WANG
1
;
Yiyuan HU
Author Information
1. 北京中医药大学东直门医院骨科，北京 100700
- Keywords:
Artificial intelligence;
Nursing education;
CiteSpace;
Visual analysis;
Research hotspots
- From:
Chinese Journal of Practical Nursing
2024;40(32):2537-2544
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To discuss the current research status, research hotspots and future trends of artificial intelligence (AI) in the field of nursing education, and provide insights for nursing education researchers, practitioners and policy makers.Methods:The computer searched the literature related to AI and nursing education in Web of Science and China National Knowledge Infrastucture from January 1, 2014 to December 31, 2023. CiteSpace software was used to carry out visual analysis of the annual publication trend, institution distribution and literature data, including keyword co-occurrence, cluster analysis and outburst word analysis, and to explore the research status and trend of AI in the field of nursing education through comprehensive analysis of these data.Results:A total of 934 articles were included, including 769 in English and 165 in Chinese. The analysis of publication trends indicated a general increase in the volume of papers on nursing education both domestically and internationally. The institutional distribution analysis revealed that the number and centrality of AI publications in English literature in the field of nursing education was higher than that in Chinese literature, among which Harvard University had the highest number of publications (26) and the University of California system had the highest centrality index (0.22). The top 10 institutions in terms of Chinese publication volume all published 2 articles, and the centrality index was 0. Co-occurrence and cluster analysis of keywords revealed that research hotspots in AI technology within the field of nursing education primarily focus on three dimensions: enhancing learning experiences, optimizing teaching models, and applications in high-risk nursing scenarios. The research themes concentrated on implementing diverse artificial intelligence technologies to innovate methods of nursing education and training, alongside their application in high-risk nursing scenarios and specialized care requirements. The breakout word analysis revealed emerging trends in nursing education: widespread use of augmented reality and simulation, data-driven nursing education and risk management, intelligent use of electronic health records, preventive health care, and attitudinal change.Conclusions:AI is emerging as a pivotal catalyst in the evolution of nursing education, with substantial scope for domestic researchers to venture further. Future inquiries should delve into the synthesis of simulation technologies, amplify the depth and breadth of data analytics, and execute customized learning stratagems, andto comprehensively improve the quality of nursing education.
