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The Associations of AI Literacy, AI Self-Efficacy and AI Attitudes Among Nursing Students: A Cross-Sectional Path Analysis

Year: 2026

Type: article

Abstract: Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into nursing education, yet structured AI literacy training and ethical guidance remain limited. Consequently, nursing students often rely on informal learning, resulting in variability in AI readiness, confidence, and responsible use. Aims: The study examined (1) whether AI literacy was positively associated with AI self-efficacy and AI attitudes and (2) whether AI self-efficacy mediated the relationship between AI literacy and AI attitudes. Methods: A cross-sectional survey using convenience sampling was conducted with 100 prelicensure nursing students in New York City. Data were collected using the AI Literacy Scale (AILS), AI Self-Efficacy Scale (AISES), and Generative AI Attitude Scale (GAIAS). Correlation and path analyses were conducted using Amos 30.0. Results: Participants had a mean age of 30.25 years, and 71% were women. AI literacy and AI self-efficacy were both positively associated with AI attitudes (all p < .001). Path analysis showed that AI literacy significantly predicted AI self-efficacy (β = .39, p < .001) and AI attitudes (β = .28, p = .003). AI self-efficacy significantly predicted AI attitudes (β = .31, p = .001) and partially mediated the relationship between AI literacy and AI attitudes. The model explained 24% of the variance in AI attitudes. Conclusions: AI self-efficacy partially mediated the relationship between AI literacy and AI attitudes, supporting Social Cognitive Theory in explaining nursing students' readiness for GenAI. Educational strategies that strengthen AI literacy and AI self-efficacy may facilitate the ethical and effective integration of GenAI into nursing education.

Source: Nursing Reports

DOI: 10.3390/nursrep16080292

Authors: ShinHi Han, Hee Sun Kang, Philip Gimber, Sunghyun Lim

Institutions: LaGuardia Community College, City University of New York, Chung-Ang University, Columbia University Irving Medical Center

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Quality education, Good health and well-being

Open Access status: gold

Estimated APC paid: $2,172