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The integration of artificial intelligence (AI) in nursing education is crucial for enhancing technological adaptability. However, the relationship between nursing students’ AI self-efficacy, attitudes, and educational level (associate vs. bachelor’s degrees) remains underexplored. Previous studies predominantly rely on linear models, which inadequately capture the complex dynamics of these factors. This multicenter, cross-sectional study was conducted from February to March 2025 across 93 medical institutions in 13 provinces in China. A total of 1, 113 nursing students (748 with associate degrees and 365 with bachelor’s degrees) participated. Data were collected using the Artificial Intelligence Self-Efficacy Scale (AISES) and the General Attitudes toward AI Scale (GAAIS). A network model was constructed through LASSO-regularized partial correlation analysis to assess node strength, bridge strength, and predictability. Differences between the two educational groups were analyzed using the Network Comparison Test. The 24-node network revealed 156 valid connections (56. 5%). AI₄ (AI tone matches humans) emerged as the central hub (strength = 2. 145), while AI₁ (AI interaction vivid) demonstrated the strongest bridging (bridge strength = 3. 255). The Network Comparison Test indicated no significant difference in global network structure (M = 0. 255, P = 0. 476) or global strength (S = 0. 407, P = 0. 287) between educational groups. However, significant local differences were found: the bachelor’s degree group showed stronger connections related to technical transparency (E = 0. 255), suggesting a more integrated understanding of AI systems. Conversely, the associate degree group exhibited a significant negative association between programming confidence and risk anxiety (E = 0. 087), indicating a potential competence-anxiety tension that warrants targeted support. The AI self-efficacy and attitudes of nursing students form a “cognitive-affective-skill” network, with anthropomorphic interactions serving as a key factor. While the cross-sectional design precludes causal inference, these findings suggest the need for stratified educational strategies: enhancing safety simulations for associate degree students and integrating technical principles for bachelor’s degree students. Such targeted interventions could foster technological empowerment and promote educational equity.
Zeng et al. (Mon,) studied this question.