The Relationship Between Nursing Undergraduates' Artificial Intelligence Readiness, Learning Motivation, and Artificial Intelligence Anxiety: A Mediation and Network Analysis Study

Background Artificial Intelligence (AI) is profoundly transforming the field of healthcare, while also inducing adaptive anxiety among healthcare professionals. As the future backbone of the nursing workforce, nursing undergraduates' Artificial Intelligence Anxiety (AIA) and Artificial Intelligence Readiness (AIR) may significantly influence their professional development and career transition. Learning Motivation (LM), as a critical psychological factor, may play a key role in enhancing AIR while alleviating AIA. Objective This study explores the relationship between AIR and Artificial AIA among nursing undergraduates in China, examines the mediating role of LM, and further analyzes the intrinsic psychological structure of the three variables through network analysis. Methods A cross-sectional design was employed to recruit 738 nursing undergraduates from three medical universities in Hubei Province, China, between March and May 2025. The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), Learning Motivation Scale (LMS), and Artificial Intelligence Anxiety Scale (AIAS) were used to measure AIR, LM, and AIA. Mediation analysis was conducted using the SPSS 27 PROCESS macro (Model 4), and network analysis was utilized to estimate the network structure of AIR, LM, and AIA. Visualization and centrality measures were performed using R packages. Results The results indicated that: (1) The mean scores for AIR, LM, and AIA among nursing undergraduates in China were 75.64 (SD 7.12), 64.99 (SD 4.62), and 75.71 (SD 9.35), respectively suggesting that AIR was at a moderately high level, LM at a moderate level, and AIA at a moderately high level. (2) AIR was significantly negatively correlated with AIA (r = − 0.64, P < 0.001), and significantly positively correlated with LM (r = 0.47, P < 0.001), while LM was significantly negatively correlated with AIA (r = − 0.50, P < 0.001). (3) LM played a significant partial mediating role between AIR and AIA (-0.16, 95% CI = -0.22, -0.11), accounting for 19.4% of the total effect. (4) In the AIR, LM, and AIA network model, Sociotechnical Blindness (betweenness = 1.17, closeness = 0.83, strength = 1.59), Intrinsic Motivation (betweenness = -0.72, closeness = 0.33, strength = 0.20), and Vision (betweenness = 1.55, closeness = -0.99, strength = 0.29) were identified as core symptoms, while Intrinsic Motivation, Ability, and Learning were bridge symptoms. Conclusion This study reveals the current status and interrelationships of AIR, LM, and AIA among nursing undergraduates in China. The findings provide important insights for optimizing nursing education strategies. Clinical trial number Not applicable.