Network structure of artificial intelligence anxiety among nursing students and educational implications: A multicenter network analysis

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Authors: Qin Zeng, Jun Zhu, Jiacheng Hu, Shaoyu Su, Min Yang, Biru Luo, Yan Huang
Cited by: 1
References: 52

Abstract

BACKGROUND: While artificial intelligence (AI) transforms nursing practice, nursing students experience profession-specific AI anxiety. This study examines the network structure of such anxiety and its association with learning needs. OBJECTIVE: This study aims to describe the network structure of AI anxiety among nursing students, compare anxiety network differences between students with associate degree or below and those with bachelor's degree or higher, and explore the association between anxiety and learning needs. DESIGN: A multi-center cross-sectional survey using stratified convenience sampling. SETTING: Schools of nursing within 93 medical universities from 13 provinces across China's five major geographic regions (North, South, East, Western, and Central China), representing diverse nursing education environments. PARTICIPANTS: 1253 nursing students were recruited from May to June 2025. METHODS: The study used a general information survey, the Artificial Intelligence Anxiety Scale (AIAS; 21 items, 4 dimensions), and a learning needs assessment. Gaussian graphical network and bridge centrality analysis identified core symptoms and cross-dimensional pathways. Group comparisons used permutation-based network invariance testing. RESULTS: = 0.925). Students with associate degrees or lower demonstrated stronger cross-dimensional anxiety connections (e.g., fear of robot autonomy → job displacement, P = 0.022), while fear of job displacement was positively correlated with learning motivation (edge weight = 0.29). The network displayed excellent stability (CS coefficient = 0.75). CONCLUSIONS: AI anxiety among nursing students forms a stable and interconnected network, with profession-specific hubs. Targeted interventions should prioritize procedural learning anxiety and ethical misuse concerns, while using occupational threats as a catalyst for learning. Curriculum reform must address the higher susceptibility of associate degree or below education students to anxiety spillover effects.

Fields of Study & Subjects

Mental Health Research Topics COVID-19 and Mental Health Resilience and Mental Health

Publication details

Journal / source
Nurse Education Today
Publisher
Elsevier BV
Volume
165
Pages
107177–107177
Publication date
20 May 2026
Language
EN