Exploring artificial intelligence (AI) Chatbot usage behaviors and their association with mental health outcomes in Chinese university students
Abstract
Technology dependence has long been a critical public health issue, especially among young people. With the development of AI chatbots, many individuals are integrating these tools into their daily lives. However, we have limited knowledge about the issues related to AI chatbot usage and dependence. Therefore, we conducted a cross-sectional survey to investigate AI chatbot usage behaviors and their association with mental health outcomes among Chinese university students. A total of 1004 students who met the inclusion criteria were included in the analysis. Our survey revealed that 45.8 % of students reported using AI chatbots in the last month, with most using them one to three days per week (78.5 %) and showing light (38.2 %) to moderate (37.6 %) dependence. University students who use AI chatbots exhibited significantly higher levels of depression (p 0.05). Nevertheless, resilience may be indirectly linked to AI chatbot usage/dependence via depression (β = -0.077 to -0.052; p < 0.05). In conclusion, AI chatbot usage is relatively common among university students, though overuse and severe dependence are infrequent. AI chatbot usage and dependence are associated with higher levels of depression; however, the causal relationship warrants further investigation.
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Among 1000 LLM-CB users, 76% showed light to moderate dependence measured by questionnaire.
“Zhang et al. found that out of 1000 LLM-CB users, 76% showed light to moderate dependence measured by their questionnaire126.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [126]
Survey studies (4/36) show first insights into prevalence of problematic AI use, with over 76% of regular LLM-CB users showing mild to moderate problematic use.
“Survey studies (4/36) show first insights into prevalence of problematic AI use, with over 76% of regular LLM-CB users showing mild to moderate problematic use126.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [126]
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A scoping review on the mental health harms of LLM-based chatbots
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- Authors
- Zhang, X., Li, Z., Zhang, M., Yin, M., Yang, Z., Gao, D., & Li, H.
- Affiliations
- Universidad de Granada (ES)
- Jishou University (CN)
- Shanghai University of Sport (CN)
- Fuyang Normal University (CN)
- Chongqing University of Posts and Telecommunications (CN)
- Sichuan Agricultural University (CN)
- Corresponding author
- Li, Hansen
- Venue
- Journal of Affective Disorders
- Publisher
- Elsevier BV
- Published
- 2025-07
- Volume
- 380
- Pages
- 394-400
- ISSN
- 0165-0327 (ISSN-L)
- OpenAlex
- W4408878646
- Cited by
- 56
- References
- 18
- Citations by year
- 2026: 48 · 2025: 8
- FWCI
- 97.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Affective Disorders (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Gender Studies › Gender and Technology in Education
- Keywords
- AI chatbots, technology dependence, depression, mental health, university students, resilience, psychological well-being, Chinese students
- MeSH terms
- Internet Addiction Disorder, Generative Artificial Intelligence, Adolescent, Adult, Artificial Intelligence, China, Cross-Sectional Studies, Depression, Female, Humans, Male, Mental Health, Surveys and Questionnaires, Students, Universities, Resilience, Psychological, Young Adult
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