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Clinical and sociodemographic predictors of AI use for mental health among college students

Liu, Cindy H., Zhang, Wenbo, Lou, Felix, Zhao, Chang, Chow, Angela, Yip, Tiffany·2026·Journal of Affective Disorders
DOI 10.1016/j.jad.2026.122058
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Abstract

Background Generative AI tools are increasingly accessible to college students, yet little is known about who uses them for mental health support. This study examined predictors of AI use for mental health among college students at two U.S. institutions. Methods Data were drawn from students (n = 896) who completed an AI module as part of the 2024-2025 Healthy Minds Study. The analytic sample comprised 675 students with complete data. Descriptive analyses compared three groups: never use AI, use AI but not for mental health, and use AI for mental health. Hierarchical logistic regression examined predictors of AI use for mental health using a binary outcome (AI use for mental health vs. no AI use for mental health), as the three-group structure could not accommodate general AI use as a predictor due to structural confounding. A supplementary multinomial logistic regression compared all three groups without general AI use. Results Approximately 18% of students reported using AI for mental health. The never-use-AI group had higher proportions of non-binary/other gender and LGBQ+ students; the proportion of Asian students increased across groups in a stepwise pattern; and the AI-not-for-MH group showed better mental health profiles than both other groups. In regression models, frequent general AI use was the strongest predictor (OR = 11.42-12.87). Moderate depression (OR = 2.06), severe depression (OR = 2.49), severe anxiety (OR = 2.04), and suicidality (OR = 1.97) each predicted…

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  • Sexual orientation did not predict using AI for mental health once symptom severity and general AI use were taken into account.

    “sexual orientation did not predict using AI for mental health once symptom severity and general AI use were taken into account.”

    From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Liu et al., 2026

  • In a survey of 675 students at two US universities, Liu et al. (2026) found that LGBQ+ and non-binary students were overrepresented among those who had never used generative AI.

    “In a survey of 675 students at two US universities, Liu et al. (2026) found that LGBQ+ and non-binary students were overrepresented among those who had never used generative AI, and sexual orientation did not predict using AI for mental health once symptom severity and general AI use were taken into account.”

    From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Liu et al., 2026

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Metadata

Authors
Liu, C. H., Zhang, W., Lou, F., Zhao, C., Chow, A., & Yip, T.
Venue
Journal of Affective Disorders
Publisher
Elsevier BV
Published
2026-11
Volume
412
Pages
122058
ISSN
0165-0327
DOI
10.1016/j.jad.2026.122058
Cited by
4
References
10
Language
en
Licence

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