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Latent profile analysis of artificial intelligence anxiety among undergraduate nursing students: a cross-sectional
Jiali Xu1, Xiaoqin Yu1, Zhen Fang1
1Jiujiang University Medical College, Jiujiang, China.
Aim/Objective:
This study aimed to classify subgroups of artificial intelligence (AI) anxiety among nursing undergraduates using latent profile analysis, describe their demographic characteristics, and identify factors associated membership in each latent profile.
Background:
Artificial intelligence (AI) has been widely implemented in clinical nursing practice, but nursing undergraduates often experience AI anxiety, which impedes their adaptation to smart medical services. Few existing studies have stratified students according to anxiety levels to formulate targeted intervention measures.
Design:
A cross-sectional study.
Methods:
Participants were 526 nursing undergraduates recruited from several medical colleges in Jiangxi, Hunan, Hubei, and Sichuan provinces as well as Chongqing Municipality, China. Questionnaires, including a general information questionnaire, the Artificial Intelligence Anxiety Scale, and the Artificial Intelligence Self-Efficacy Questionnaire, were distributed. Mplus version 8.3 was used for latent profile analysis, and multinomial logistic regression was performed to analyze factors associated with profile membership.
Results:
A total of 526 students were included. The total AI anxiety score was 57.76 ± 15.94, indicating a moderate level of AI anxiety. AI anxiety among undergraduate nursing students was classified into three latent profiles: the low AI anxiety group (53.2%), moderate AI anxiety group (12.7%), and high AI anxiety group (34.0%). Multinomial logistic regression analysis showed that gender, participation in AI-related education or courses, attitude toward AI, and monthly household income (Renminbi [RMB]) were independently associated with membership in latent profiles among undergraduate nursing students with AI anxiety (p < 0.05).
Conclusion:
Nursing undergraduates present distinct heterogeneous patterns of AI anxiety. These findings may help identify students with elevated AI anxiety and inform individualized educational interventions.
