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Artificial intelligence literacy in higher education and implications for nursing education: A scoping review
Zhijuan Lai1, Ting Zhang1, Nan Huang1
1School of Nursing, Guangdong Pharmaceutical University, 283 Jianghai Avenue, Guangzhou, 510310, China.
Background:
Despite the growing body of studies focusing on the development of artificial intelligence literacy in higher education, the status of AI literacy among nursing students remains understudied.
Objective:
This study aims to conduct a scoping review of the conceptual frameworks, current status, influencing factors, assessment tools, and teaching method strategies related to artificial intelligence literacy in higher education.
Methods:
This scoping review followed Arksey and O'Malley's methodology framework and the PRISMA-ScR guidelines. We searched nine electronic databases (Web of Science, Elsevier, CINAHL, PubMed, IEEE Xplore, Wanfang Data, CNKI, VIP, and CBM) from the database inception to July 1st, 2025. For studies that met the inclusion criteria, descriptive statistical methods were used to summarize key characteristics, including first author, year of publication, country or region, study design, study population, sample size, theme of AI literacy, and aims.
Findings:
Our scoping review identified 52 eligible studies, which were categorized as follows: experimental studies (n = 8), cross-sectional studies (n = 39), and mixed-methods studies (n = 5). The thematic distribution revealed the following: (1) conceptual frameworks of artificial intelligence literacy (n = 9), (2) current status of artificial intelligence literacy level among college students (n = 39), (3) development for artificial intelligence literacy assessment tools (n = 5), (4) factors influencing artificial intelligence literacy among college students (n = 17), and (5) teaching methods for cultivating artificial intelligence literacy (n = 8).
Conclusion:
Current studies on artificial intelligence literacy (encompassing conceptual frameworks, current status, influencing factors, assessment tools, and teaching method strategies) provide a robust evidence base for the innovation of nursing education. The systematic integration of artificial intelligence into nursing curricula represents an inevitable trajectory for contemporary healthcare education. Cultivating artificial intelligence literacy among nursing students requires coordinated efforts across governmental, institutional, and societal domains.
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