Artificial Intelligence in Nursing Education: a Bibliometric Analysis of Trends, Challenges, and Future Directions

Objective This study aims to perform, for the first time, a comprehensive bibliometric analysis of artificial intelligence (AI), machine learning, and ChatGPT in nursing education. Methods The Scopus database was employed to retrieve data and later analyzed on Vosviewer and R Studio, Results A total of 101 documents were analyzed, spanning from 1991 to 2024, sourced from 64 different journals. The annual growth rate of the documents was 11.07%, with an average document age of 2.28 years. The average number of citations per document was 8.90, and each document had an average of 4.53 co-authors. Among the authors, Ahn J contributed to 3 documents, the leading institution was the National University of Singapore with 14 publications, the United States had the highest number of publications with 43. Co-words analysis categorized the focus of 101 papers into 20 distinct groups. Conclusion The study provides an extensive overview of research trends in AI and machine learning within nursing education.