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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

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Artificial Intelligence in Nursing Education: A Bibliometric Analysis.

Xiaoyue Yu^1,2, Weizhen Wu¹, Jun Ni³

¹Department of Obstetrics and Gynecology, Department of Nursing, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, Guangdong-HongKong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China, gzhmc.edu.cn.

Nursing Research and Practice

|February 13, 2026

Summary

Artificial intelligence (AI) offers promising avenues for nursing education, enhancing practitioner preparation. This bibliometric analysis identifies key trends and contributors in AI in nursing education research.

Keywords:

AI artificial intelligence database hotspot nursing students

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Area of Science:

Bibliometrics
Artificial Intelligence in Education
Nursing Education

Background:

Artificial intelligence (AI) is rapidly advancing across industries.
AI presents significant potential for improving nursing education and preparing future practitioners.
The healthcare landscape necessitates continuous adaptation in nursing training.

Purpose of the Study:

To conduct a bibliometric analysis of research on artificial intelligence in nursing education.
To identify key trends, influential sources, and emerging topics in this field.
To provide insights for future research and development in AI-enhanced nursing education.

Main Methods:

Bibliometric analysis utilizing VOSviewer, CiteCite, and the Bibliometrix R package.
Visualization of publication data to identify patterns and trends.
Analysis of keywords, sources, and contributing institutions.

Main Results:

A total of 430 publications were analyzed.
The United States and China are leading contributors, with the National University of Singapore as a prominent institution.
Key journals include Nurse Education Today and Nurse Education in Practice.
Trending topics include technology, educational programs, and simulation.

Conclusions:

This study offers novel perspectives on the integration of AI in nursing education.
Findings can guide scholars interested in AI's role in training future nurses.
Highlights the growing importance of technology and simulation in nursing curricula.