Knowledge and attitudes of nursing students towards artificial intelligence and related factors: A systematic review - Stephanie Sandanasamy; Philip McFarlane; Yu Okamoto; Alannah L. Couper (Journal of Nursing Reports in Clinical Practice, 2025)
This systematic review aimed to evaluate the knowledge and attitudes of nursing students towards artificial intelligence (AI) and to identify factors associated with their perspectives. A comprehensive search was conducted across various international electronic databases, including Scopus, PubMed, and Web of Science. Keywords were derived from Medical Subject Headings (MeSH) and included terms such as "knowledge", "attitude", "artificial intelligence", and "nursing students". The search encompassed records from the earliest available date up to July 20, 2024. The selected studies were evaluated for quality using the Appraisal tool for Cross-Sectional Studies (AXIS tool), an appraisal instrument designed for cross-sectional studies. In total, 1,299 nursing students were included across six cross-sectional studies. Among the participants, 80.87% were female, with a mean age of 22.01 (standard deviation [SD]=3.05) years. The studies incorporated in this systematic review were conducted in Egypt (n=3), India (n=1), the United States (n=1), and Croatia (n=1). The average knowledge score about AI among nursing students in the three studies was 66.62 out of 100, reflecting good knowledge. Students' knowledge of digital transformation, digital skills, and digital health literacy had a significant positive relationship (n=1). The average positive attitude of nursing students towards AI, as observed in six studies, was 64.73 out of 100, indicating a generally high positive attitude among nursing students regarding AI. There was a significant relationship between male gender (n=1), adoption of AI technology, and knowledge (n=1) with a positive attitude about AI. Nursing policymakers and managers can enhance nursing students' knowledge and attitudes toward AI by focusing on digital transformation, digital skills, digital health literacy, male gender, AI technology adoption, and knowledge.