Artificial intelligence-driven nursing examination research: dynamic evolution and hotspot analysis

Abstract

OBJECTIVE: This study systematically combed the current status of the application of artificial intelligence technology in the field of nursing education assessment, and presented the research hotspots in the field visually through knowledge mapping. METHODS: A comprehensive search was conducted in the core database of Web of Science, and the number of publications, countries and keywords were visualized and analyzed using software such as Cite Space (6.2. R3) and VOS viewer (1.6.20). RESULTS: A total of 903 relevant publications were included in this paper, with a general upward trend in the growth of literature on AI applied to the field of nursing examinations, with the main output country being the United States. Among them, ‘Item Response Theory’ is the hottest research topic, and the recent and future research hotspots will focus on hot areas such as ‘Clinical Decision Support System’. CONCLUSION: The application of artificial intelligence in the field of nursing examination is becoming more and more widespread, which helps to improve the efficiency and quality of nursing educators.

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