Depression and Artificial Intelligence Anxiety Among Chinese University Students: A Bayesian Network Analysis

Resumo

Background The rapid advancement of artificial intelligence (AI), while presenting opportunities for higher education, has also triggered adverse psychological consequences such as depression and AI anxiety (AIA) among Chinese university students. However, the symptom‐level associational structure and potential directional dependencies between these two constructs remain unclear. Methods In November 2025, 1610 Chinese university students were recruited online. The Patient Health Questionnaire‐9 (PHQ‐9) and the AIA scale (AIAS) were used to measure symptoms of depression and AIA, respectively. Undirected network analysis and Bayesian network analysis were employed to explore the associations between depression and AIA symptoms. Results Undirected network analysis revealed that PHQ4 (“Fatigue”) and PHQ6 (“Worthlessness”) were central symptoms. PHQ4 and AIA8 (“Falling behind in AI”) were identified as bridge symptoms connecting depression and AIA. Bayesian network analysis further suggested that PHQ4 is a pivotal symptom in the potential directional association between depression and AIA. Conclusion This study provides symptom‐level evidence for the association and potential directional dependencies between depressive symptoms and AIA among Chinese university students. PHQ4 and AIA8 may play important roles in linking depressive symptoms with AIA symptoms, suggesting that they may represent potential targets for future longitudinal and intervention studies.

Tipo

Artigo

Área

Psicologia

Ano

2026

Idioma

Inglês

Licença

CC BY

DOI

10.1155/da/8705382

Como citar (ABNT)

JIN, Kangyue; WU, Yuntena; JIN, Tonglin. Depression and Artificial Intelligence Anxiety Among Chinese University Students: A Bayesian Network Analysis. Depression and Anxiety, v. 2026, n. 1, 2026. DOI: 10.1155/da/8705382.