Related Experiment Video
Updated: Jul 10, 2026
Artificial Intelligence-Based System for Detecting Attention Levels in Students
Published on: December 15, 2023
Depression and Artificial Intelligence Anxiety Among Chinese University Students: A Bayesian Network Analysis
Kangyue Jin1, Yuntena Wu1, Tonglin Jin1
1School of Psychology, Inner Mongolia Normal University, Hohhot, Inner Mongolia Autonomous Region, China, imnu.edu.cn.
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.