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

Area of Science:

• Psychology
• Artificial Intelligence Studies
• Higher Education

Background:

• Rapid AI advancement presents challenges for higher education.
• Adverse psychological effects like depression and AI anxiety (AIA) are emerging among Chinese university students.
• The symptom-level structure and directional links between depression and AIA remain unclear.

Purpose of the Study:

• To investigate the symptom-level associations between depression and AIA in Chinese university students.
• To explore potential directional dependencies between depressive symptoms and AIA.
• To identify key symptoms that link depression and AIA.

Main Methods:

• Recruited 1610 Chinese university students online in November 2025.
• Utilized the Patient Health Questionnaire-9 (PHQ-9) for depression symptoms and the AIA scale (AIAS) for AI anxiety.
• Employed undirected network analysis and Bayesian network analysis to examine symptom associations.

Main Results:

• Fatigue (PHQ4) and worthlessness (PHQ6) emerged as central depressive symptoms.
• Fatigue (PHQ4) and falling behind in AI (AIA8) acted as bridge symptoms connecting depression and AIA.
• Bayesian network analysis indicated Fatigue (PHQ4) as a pivotal symptom in the directional association between depression and AIA.

Conclusions:

• The study provides symptom-level evidence for the association and potential directional links between depression and AIA.
• Fatigue (PHQ4) and falling behind in AI (AIA8) are identified as crucial linking symptoms.
• These key symptoms may serve as targets for future longitudinal and intervention studies on AI-related psychological distress.