Latent profile analysis of artificial intelligence anxiety among undergraduate nursing students: a cross-sectional study
- 1Jiujiang University Medical College , ,
- 2Department of Oncology, Southwest Jiaotong University, The Third People’s Hospital of Chengdu , , ,
Abstract
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.
Related
- Knowledge, attitudes, practices, and perceived barriers regarding artificial intelligence among undergraduate nursing students in Somalia: A cross-sectional study
- Assessment of Knowledge, Attitudes, and Perceptions Regarding Artificial Intelligence Among Undergraduate Medical Students.
- Artificial intelligence-assisted training for proximal caries diagnosis by undergraduate dental students: a randomized experimental study.
- Generative artificial intelligence–assisted medical self-care and associated factors among undergraduate students at Arsi University, South-Eastern Ethiopia
- Artificial intelligence use patterns, attitudes, and psychological distress among medical students: a cross-sectional survey
BibTeX
@article{xu2026latent,
title = {{Latent profile analysis of artificial intelligence anxiety among undergraduate nursing students: a cross-sectional study}},
author = {Jiali Xu and Xiaoqin Yu and Zhen Fang and Xiaoying Wang and Xianxian Xing and Juanjuan Cao and Xin Yang},
journal = {Frontiers in public health},
year = {2026},
month = sep,
doi = {10.3389/fpubh.2026.1971125},
url = {https://europepmc.org/article/PMC/PMC13661933},
note = {CC BY},
}
RIS
TY - JOUR
TI - Latent profile analysis of artificial intelligence anxiety among undergraduate nursing students: a cross-sectional study
AU - Jiali Xu
AU - Xiaoqin Yu
AU - Zhen Fang
AU - Xiaoying Wang
AU - Xianxian Xing
AU - Juanjuan Cao
AU - Xin Yang
PY - 2026
DA - 2026/09/30
JO - Frontiers in public health
DO - 10.3389/fpubh.2026.1971125
UR - https://europepmc.org/article/PMC/PMC13661933
AB - 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.
SN - 2296-2565
ER -