ChatGPT for mental health support: A cross-sectional survey of university students
University students face growing mental health challenges, yet continue to encounter barriers to in-person care, including access, stigma, cost, and limited capacity. As artificial intelligence (AI) becomes integrated into daily life, tools such as ChatGPT are increasingly explored by students for emotional support. This study examined how university students perceive the benefits, limitations, and potential utility of ChatGPT and other AI platforms as tools for mental health support. A cross-sectional survey was conducted among 392 students at the University of British Columbia, in which 259 (66.6%) had used ChatGPT for mental health. Quantitative data was analyzed using descriptive statistics, and qualitative responses underwent thematic exploration. Most students viewed ChatGPT as somewhat trustworthy and helpful for mental health-related questions, valuing its accessibility and non-judgmental space for emotional expression. Compared with professional support, 52.1% perceived AI as similarly useful, while 23.6% perceived it as better suited in certain situations, such as moments of hesitation to seek help. Non-users most frequently cited privacy concerns, inaccurate responses, and lack of human empathy as reasons for avoidance. Key limitations included reduced likelihood to seek professional help and limited human-like support. Overall, findings suggest that AI tools, such as ChatGPT, may serve as low-barrier emotional outlets and resources that complement counseling. Integration within academic settings may help bridge unmet needs, provided that ethical and privacy safeguards are prioritized. Further longitudinal research is warranted to clarify ChatGPT’s evolving role in digital mental health and its long-term impact on student wellbeing.
- Sheldon E, Simmonds-Buckley M, Bone C. Prevalence and risk factors for mental health problems in university undergraduate students: A systematic review with meta-analysis. J Affect Disord. 2021;287(1):282-292. doi: 10.1016/j.jad.2021.03.054
- Lipson SK, Zhou S, Abelson S, et al. Trends in college student mental health and help-seeking by race/ethnicity: Findings from the national healthy minds study, 2013–2021. J Affect Disord. 2022;306:138-147. doi: 10.1016/j.jad.2022.03.038
- Storrie K, Ahern K, Tuckett A. A Systematic review: Students with Mental Health problems-A Growing Problem. Int J Nurs Pract. 2010;16(1):1-6. doi: 10.1111/j.1440-172x.2009.01813.x
- Duffy ME, Twenge JM, Joiner TE. Trends in Mood and Anxiety Symptoms and Suicide-Related Outcomes Among U.S. Undergraduates, 2007–2018: Evidence From Two National Surveys. J Adolesc Health. 2019;65(5):590-598. doi: 10.1016/j.jadohealth.2019.04.033
- Solmi M, Radua J, Olivola M, et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry. 2022;27(1):281- 295. doi: 10.1038/s41380-021-01161-7
- Ebert DD, Mortier P, Kaehlke F, et al. Barriers of mental health treatment utilization among first‐year college students: First cross‐national results from the WHO World Mental Health International College Student Initiative. Int J Methods Psychiatr Res. 2019;28(2):e1782. doi: 10.1002/mpr.1782
- Ballout S. Trauma, Mental Health Workforce Shortages, and Health Equity: A Crisis in Public Health. Int J Environ Res Public Health. 2025;22(4):620. doi: 10.3390/ijerph22040620
- Bell IH, Nicholas J, Broomhall A, et al. The impact of COVID-19 on youth mental health: A mixed methods survey. Psychiatry Res. 2023;321:115082. doi: 10.1016/j.psychres.2023.115082
- World Health Organization. COVID-19 pandemic triggers 25% increase in prevalence of anxiety and depression worldwide. Posted 2022. vailable from: https://www.who. int/news/item/02-03-2022-covid-19-pandemic-triggers- 25-increase-in-prevalence-of-anxiety-and-depression-worldwide [Last accessed on November 3, 2025].
- Ennis E, McLafferty M, Murray E. Readiness to change and barriers to treatment seeking in college students with a mental disorder. J Affect Disord. 2019;252:428-434. doi: 10.1016/j.jad.2019.04.062
- Hartrey L, Denieffe S, Wells JSG. A systematic review of barriers and supports to the participation of students with mental health difficulties in higher education. Ment Health Prev. 2017;6:26-43. doi: 10.1016/j.mhp.2017.03.002
- Weissinger G, Ho C, Ruan-Iu L, Van Fossen C, Diamond G. Barriers to mental health services among college students screened in student health: A latent class analysis. J Am Coll Health. 2024;72(7):2173-2179. doi: 10.1080/07448481.2022.2104614
- Olawade DB, Wada OZ, Odetayo A, David-Olawade AC, Asaolu F, Eberhardt J. Enhancing mental health with Artificial Intelligence: Current trends and future prospects. J Med Surg Public Health. 2024;3:100099. doi: 10.1016/j.glmedi.2024.100099
- Sinha C, Meheli S, Kadaba M. Understanding Digital Mental Health Needs and Usage With an Artificial Intelligence–Led Mental Health App (Wysa) During the COVID- 19 Pandemic: Retrospective Analysis. JMIR Form Res. 2023;7(1):e41913. doi: 10.2196/41913
- Mehta A, Niles AN, Vargas JH, Marafon T, Couto DD, Gross JJ. Acceptability and Effectiveness of Artificial Intelligence Therapy for Anxiety and Depression (Youper): Longitudinal Observational Study. J Med Internet Res. 2021;23(6):e26771. doi: 10.2196/26771
- Luo X, Ghosh S, Tilley JL, Besada P, Wang J, Xiang Y. “Shaping ChatGPT into my Digital Therapist”: A thematic analysis of social media discourse on using generative artificial intelligence for mental health. Digit Health. 2025;11. doi: 10.1177/20552076251351088
- Bandara R, Fernando M, Akter S. The Privacy Paradox in the Data-Driven Marketplace: The Role of Knowledge Deficiency and Psychological Distance. Procedia Comput Sci. 2017;121:562-567. doi: 10.1016/j.procs.2017.11.074
- Lee S, Rheu M (MJ), Zhuang J. The ChatGPT Effect: Investigating Shifting Discourse Patterns, Sentiment, and Benefit–Challenge Framing in AI Mental Health Support. Behav Sci. 2025;15(9):1172. doi: 10.3390/bs15091172
- Yonatan-Leus R, Brukner H. Comparing perceived empathy and intervention strategies of an AI chatbot and human psychotherapists in online mental health support. Couns Psychother Res. 2025;25(1):e12832. doi: 10.1002/capr.12832
- Elyoseph Z, Hadar-Shoval D, Asraf K, Lvovsky M. ChatGPT outperforms humans in emotional awareness evaluations. Front Psychol. 2023;14. doi: 10.3389/fpsyg.2023.1199058
- Alanezi F. Assessing the effectiveness of ChatGPT in delivering mental health support: A qualitative study. J Multidiscip Healthc. 2024;17:461-471. doi: 10.2147/JMDH.S447368
- Kavitha K, Joshith VP, Sharma S. Beyond text: ChatGPT as an emotional resilience support tool for Gen Z – A sequential explanatory design exploration. e-Learn Digit Media. 2024. doi: 10.1177/20427530241259099
- Li Z, Zhu Z, Gui X, Luo Y. This is human intelligence debugging artificial intelligence”: Examining how people prompt GPT in seeking mental health support. Int J Human– Computer Stud. 2025;203:103555. doi: 10.1016/j.ijhcs.2025.103555
- OpenAI. Memory and new controls for ChatGPT. Posted February 13, 2024. Available from: https://openai.com/index/memory-and-new-controls-for-chatgpt/ [Last accessed on October 23, 2025].
- Ostrand R, Berger SE. Humans Linguistically Align to their Conversational Partners, and Language Models Should Too. OpenReview.net. Updated August 29, 2024. Available from: https://openreview.net/forum?id=JtgxazN6TM [Last accessed on October 23, 2025].
- Ivey J, Kumar S, Liu J, et al. Real or robotic? Assessing whether LLMs accurately simulate qualities of human responses in dialogue. arXiv. Preprint posted online 2024. doi: 10.48550/arXiv.2409.08330
- Hua Y, Na H, Li Z, et al. A scoping review of large language models for generative tasks in mental health care. npj Digit Med. 2025;8(1):230. doi: 10.1038/s41746-025-01611-4
- Siddals S, Torous J, Coxon A. It happened to be the perfect thing”: experiences of generative AI chatbots for mental health. Npj Ment Health Res. 2024;3(1):48. doi: 10.1038/s44184-024-00097-4
- Maurya RK, Montesinos S, Bogomaz M, DeDiego AC. Assessing the use of ChatGPT as a psychoeducational tool for mental health practice. Couns Psychother Res. 2025;25(1):e12759. doi: 10.1002/capr.12759
- Sharma M, Tong M, Korbak T, et al. Towards Understanding Sycophancy in Language Models. arXiv. Preprint posted online 2025. doi: 10.48550/arXiv.2310.13548
- Malmqvist L. Sycophancy in Large Language Models: Causes and Mitigations. In: Arai K, ed. Intelligent Computing. Switzerland: Springer Nature; 2025:61-74. doi: 10.1007/978-3-031-92611-2_5
- Hong J, Byun G, Kim S, Shu K, Choi JD. Measuring Sycophancy of Language Models in Multi-turn Dialogues. arXiv. Preprint posted online 2025. doi: 10.48550/arXiv.2505.23840
- Ezawa ID, Hollon SD. Cognitive restructuring and psychotherapy outcome: A meta-analytic review. Psychotherapy. 2023;60(3):396-406. doi: 10.1037/pst0000474
- Ciharova M, Furukawa TA, Efthimiou O, et al. Cognitive restructuring, behavioral activation and cognitive-behavioral therapy in the treatment of adult depression: A network meta-analysis. J Consult Clin Psychol. 2021;89(6):563-574. doi: 10.1037/ccp0000654
- Farber BA, Suzuki JY, Lynch DA. Positive Regard and Affirmation. In: Psychotherapy Relationships That Work. Oxford University Press; 2019:288-322. doi: 10.1093/med-psych/9780190843953.003.0008
- Yousif RB, Abd Alkreem MG, Yousif AB. Personalized Chatbot Responses using Reinforcement Learning and User Modeling. JEPS. 2024;14(4). doi: 10.32792/jeps.v14i3.462
- Nehring J, Gabryszak A, Jürgens P, et al. Large Language Models Are Echo Chambers. In: Calzolari N, Kan MY, Hoste V, Lenci A, Sakti S, Xue N, eds. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ELRA and ICCL; 2024:10117-10123. Available from: https://aclanthology.org/2024.lrec-main.884/
- Kran E, Nguyen HM, Kundu A, Jawhar S, Park J, Jurewicz MM. Darkbench: Benchmarking dark patterns in large language models. arXiv. Preprint posted online 2025. doi: 10.48550/arXiv.2503.10728
- Dohnány S, Kurth-Nelson Z, Spens E, et al. Technological folie a deux: Feedback loops between AI chatbots and mental illness. arXiv. Preprint posted online 2025. doi: 10.48550/arXiv.2507.19218
- Alhussein G, Ziogas I, Saleem S. Speech emotion recognition in conversations using artificial intelligence: a systematic review and meta-analysis. Artif Intell Rev. 2025;58(7):198. doi: 10.1007/s10462-025-11197-8
- Dorigoni A, Giardino PL. The illusion of empathy: evaluating AI-generated outputs in moments that matter. Front Psychol. 2025;16:1568911. doi: 10.3389/fpsyg.2025.1568911
- Iftikhar Z, Ransom S, Xiao A, Nugent N, Huang J. Therapy as an NLP Task: Psychologists’ Comparison of LLMs and Human Peers in CBT. arXiv. Preprint posted online 2025. doi: 10.48550/arXiv.2409.02244
- Iftikhar Z, Xiao A, Ransom S, Huang J, Suresh H. How LLM Counselors Violate Ethical Standards in Mental Health Practice: A Practitioner-Informed Framework. Proc AAAIACM Conf AI Ethics Soc. 2025;8(2):1311-1323. doi: 10.1609/aies.v8i2.36632
- Klimova B, Pikhart M. Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini-review. Front Psychol. 2025;16:1498132. doi: 10.3389/fpsyg.2025.1498132
- Cross S, Bell I, Nicholas J, et al. Use of AI in mental health care: Community and mental health professionals survey. JMIR Ment Health. 2024;11:e60589. doi: 10.2196/60589
- Spytska L. The use of artificial intelligence in psychotherapy: Development of intelligent therapeutic systems. BMC Psychol. 2025;13(1):175. doi: 10.1186/s40359-025-02491-9
- Ray PP. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet Things Cyber-Phys Syst. 2023;3:121-154. doi: 10.1016/j.iotcps.2023.04.003
- Marshall T, Reeson M, Loverock A, et al. Evidence-based Interventions for Youth With Concurrent Mental Health and Substance Use Disorders: A Scoping Review. Can J Psychiatry. 2025;70(5):347-371. doi: 10.1177/07067437241300957
