Artificial intelligence attitudes and mental health in Chinese university students: Nonlinear relationships and the mediating role of self-efficacy

doi:10.5498/wjp.124485

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Publication Name

World Journal of Psychiatry

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2220-3206

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Observational Study

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Psychiatry. Nov 19, 2026; 16(11): 124485
Published online Nov 19, 2026. doi: 10.5498/wjp.124485

Artificial intelligence attitudes and mental health in Chinese university students: Nonlinear relationships and the mediating role of self-efficacy

Si Fan, Zhi-Zhou Duan

Si Fan, Department of Public Health, Hospital Infection Control Center, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330000, Jiangxi Province, China

Zhi-Zhou Duan, Department of Preventive Health Service, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, Jiangxi Province, China

Co-first authors: Si Fan and Zhi-Zhou Duan.

Author contributions: Fan S and Duan ZZ analyzed the data and wrote the manuscript; Duan ZZ edited the manuscript. All authors read and approved the final manuscript. Fan S and Duan ZZ contributed equally to this work as co-first authors.

AI contribution statement: The authors declare that no artificial intelligence (AI) tools or large language models were used in study design or data collection, analysis, or interpretation.

Institutional review board statement: The Medical Ethics Committee of the Health Science Center, Yangtze University (Approval No. 202502001) approved this study.

Informed consent statement: All participants provided written informed consent.

Conflict-of-interest statement: There are no conflicts of interest to report.

STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.

Data sharing statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Corresponding author: Zhi-Zhou Duan, Department of Preventive Health Service, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, No. 152 Aiguolu, Nanchang 330006, Jiangxi Province, China. 2013302170005@whu.edu.cn

Received: June 17, 2026
Revised: July 23, 2026
Accepted: August 31, 2026
Published online: November 19, 2026
Processing time: 135 Days and 6 Hours

Abstract

BACKGROUND

When students perceive artificial intelligence (AI) as a threat to their career prospects (an environmental factor), this perception may elicit self-doubt (a cognitive factor), which in turn may lead to avoidance of academic challenges (a behavioral response), ultimately contributing to anxiety or depression (psychological outcomes). However, to date, the literature lacks empirical evidence supporting this viewpoint within university student samples.

AIM

To explore the associations between multidimensional AI attitudes and mental health outcomes among Chinese university students, and to examine the nonlinear relationships and the mediating role of self-efficacy in these associations.

METHODS

A cross-sectional survey was conducted among 2736 undergraduate students from China. Linear regression, restricted cubic spline (RCS) analysis, and path analysis were applied to examine the relationships between AI attitudes (anxiety, job insecurity, self-doubt, and procrastination), self-efficacy, and mental health outcomes (anxiety, depression, stress, loneliness, suicidal ideation, and non-suicidal self-injury).

RESULTS

Students’ AI attitudes showed a differentiated structure, characterized by low anxiety (9.5%), high job insecurity (50.4%), and moderate levels of self-doubt (25.6%) and procrastination (36.8%). All four AI attitude dimensions positively predicted poor mental health, with procrastination emerging as the strongest predictor. The RCS analysis revealed heterogeneous nonlinear relationships: AI-related anxiety showed nonlinearity for most outcomes, self-doubt and procrastination only for severe outcomes, while job insecurity exhibited stable linearity. Path analysis confirmed that self-efficacy played a partial mediating role in the AI attitude-mental health link.

CONCLUSION

AI attitudes are associated with complex linear and nonlinear impacts on university students’ mental health, with self-efficacy serving as a key mediating mechanism. These correlational findings provide an empirical reference for targeted mental health interventions and AI literacy education in higher education settings.

Keywords: Artificial intelligence attitudes; University students; Mental health; Self-efficacy; Nonlinear relationship

Core Tip: This study explored the associations between artificial intelligence (AI) attitudes and mental health outcomes among 2736 college students. It identified distinct linear and nonlinear relationships across four dimensions of AI attitudes, and confirmed that general self-efficacy played a partial mediating role in these associations.