Research hotspot and trends of artificial intelligence in nursing education based on knowledge graph
10.3760/cma.j.cn211501-20231126-01124
- VernacularTitle:基于知识图谱的人工智能在护理教学中的研究热点与趋势
- Author:
Shan ZHANG
1
;
Lu LIU
;
Ying WU
Author Information
1. 首都医科大学护理学院，北京 100069
- Keywords:
Artificial intelligence;
Nursing education;
Knowledge graph;
Deep learning;
Machine learning
- From:
Chinese Journal of Practical Nursing
2024;40(30):2365-2371
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To show the hot spots and trends of artificial intelligence in nursing education based on the knowledge graph, to encourage nursing educators to better integrate artificial intelligence technology into nursing education.Methods:The CiteSpace software was used to visualize the literature in the core collection database of Web of Science, including the number of articles, countries, institutions, and keywords. The retrieval time was from the establishment of the database to May 12, 2023.Results:A total of 961 articles were included in the final visualization analysis, and the number of publications showed an increasing trend year by year, especially after 2017. The United States published the most articles (33.51%, 157/961), followed by China (16.34%, 322/961), and there was cooperation among countries. Harvard University of the United States published the most articles (8.84%, 85/961). Based on keyword clustering, current studies focus on the preliminary analysis of clinical nursing teaching practice data, the technical exploration of the clinical nursing decision teaching model, and the reform and development of the personalized nursing teaching model. Future research trends included artificial intelligence, outcomes, surgery, and nursing homes.Conclusions:The application of artificial intelligence technology in nursing teaching from a simple preliminary exploration of data analysis to the technical development of a complex clinical decision support system, and then to the innovative application of personalized teaching, to guide nursing educators in teaching application scenarios continue to expand and deepen. In the future, high-quality empirical studies should be designed to explore the role of artificial intelligence in improving nursing education outcomes.
