College students have rapidly adopted generative AI, but critical questions remain about its use for mental health support. In a study co-led by investigators at Mass General Brigham , 18% of surveyed college students reported using artificial intelligence (AI) for mental health. Students with more severe mental health symptoms were more likely to do so. The findings are published in the Journal of Affective Disorders .
“College students who are most drawn to AI for mental health may also be the most vulnerable to its risks,” said lead author Cindy H. Liu, PhD, director of the Developmental Risk and Cultural Resilience Laboratory in the Mass General Brigham Departments of Pediatrics and Psychiatry. “College students who are struggling may seek out AI, and we worry that these unregulated tools could stand in for human support. At the same time, many students clearly find these tools useful, which is a reason to understand where they help and where they fall short.”
Liu and her colleagues analyzed data from the 2024–2025 Healthy Minds Study, an annual web-based survey on mental health and related experiences among U.S. college students. Among 675 students from two institutions, those with severe mental health symptoms reported AI use for mental health at rates higher than the 18% observed overall. Moderate depression, severe depression, severe anxiety, and suicidality were each associated with an approximately two-fold higher likelihood of AI use for mental health. Asian students also had about twice the odds of using AI for mental health.
“Conversations with AI for mental health may pose a risk because of how appealing they are: AI can act as a relational partner that is always available, never rejects, and offers unconditional validation,” said Liu. “We don’t yet know whether using general-purpose AI for mental health is beneficial or whether it undermines critical capacities such as emotional regulation or perspective-taking.”
The investigators provide actionable guidance. They note that AI platforms should embed crisis detection and referral mechanisms, institutions should consider how to support students who may turn to AI when formal care may feel inaccessible—a pattern seen among students with severe depression and Asian students—and mental health practices should seek to understand how patients are using these tools alongside or in place of formal care.
Authorship: In addition to Liu, Mass General Brigham authors include Wenbo Zhang, Felix Lou, and Chang Zhao. Additional authors include Angela Chow and Tiffany Yip.
Disclosures: Liu serves as an advisor for youth mental health for Surgo Health, The Asian American Foundation, and a youth-oriented project funded by The Manton Foundation. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding: None.
Paper cited: Liu CH et al. “Clinical and sociodemographic predictors of AI use for mental health among college students” Journal of Affective Disorders DOI: 10.1016/j.jad.2026.122058
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Journal of Affective Disorders
Data/statistical analysis
People
Clinical and sociodemographic predictors of AI use for mental health among college students
Liu serves as an advisor for youth mental health for Surgo Health, The Asian American Foundation, and a youth-oriented project funded by The Manton Foundation. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.