AI anxiety, AI self-competence, AI literacy, and AI self-efficacy among nursing students: A parallel mediation analysis

Abstract

Artificial intelligence (AI) is a trending topic in the educational field today, so it is also vital to understand how it affects nursing education. This study utilized a descriptive cross-sectional research design with a parallel mediation analysis to assess the levels of AI literacy (AIL), AI self-efficacy (AISE), AI anxiety (AIA), and AI self-competence (AISC) among 255 purposively chosen nursing students from a higher education institution in Olongapo City, Philippines, during the academic year 2024-2025. Data was collected through an online survey distributed from September to October 2024. The study adopted two standardized instruments, one for AIA and another for AIL, AISE, and AISC constructs. Statistical analysis included descriptive statistics and Hayes’ process macro model 4 for mediation analysis using IBM SPSS version 23. Findings revealed direct relationships between AIL and AISE, with AIA mediating this association. Additionally, AISC was found to mediate the link between AIL and AISE also. The study contributes valuable insights to the field of AI research, shedding light on the complex interplay among AIL, AIA, AISE, and AISC in the context of nursing education.

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