Summary: New research uncovers a pressing safety and policy paradox in campus healthcare: college students experiencing significant mental health crises are disproportionately turning to generative artificial intelligence for emotional support. This pattern raises urgent questions about care quality, crisis detection, and institutional responsibility.
Using data from the 2024–2025 Healthy Minds Study, researchers found that roughly 18% of surveyed college students reported using generative AI for mental health support. Critically, students with moderate-to-severe depression, severe anxiety, or active suicidality were about twice as likely as their peers to rely on these unregulated conversational systems. The finding suggests that the students most in need of clinical care are often the ones substituting human support with readily available AI tools.
Key Facts
- Vulnerability Inversion: Those with the greatest symptom burden—moderate or severe depression, severe anxiety, and suicidality—show an approximately two-fold increased likelihood of using AI for mental health support compared with students without these symptoms.
- The Appeal of Constant Availability: Generative AI is attractive because it functions like an always-on relational partner: accessible 24/7, unlikely to refuse engagement, and often offering immediate, unconditional validation. This dynamic can unintentionally interfere with students’ development of real-world emotional regulation and perspective-taking.
- Substituting for Formal Care: Investigators worry unregulated AI tools are standing in for professional counseling, especially among students facing internal barriers or structural obstacles to accessing campus mental health services.
- Cultural and Demographic Patterns: The study found that Asian students had roughly twice the odds of using AI for mental health compared with other groups, signaling the role of cultural factors and stigma in driving anonymous, digital help-seeking.
- Call for Embedded Crisis Detection: Because these platforms are acting de facto as therapy channels, the authors urge AI developers to implement mandatory, high-fidelity crisis detection and automatic referral mechanisms so that expressions of self-harm trigger prompt human intervention.
- Robust Analytics: The analysis used a standardized web-based dataset of 675 students across two institutions, offering a reliable snapshot of how students currently deploy technology to cope with distress.
- Campus Policy Implications: Rather than banning AI, universities should audit student use, ask about digital tool use during clinical encounters, and build accessible, low-barrier human alternatives where AI falls short.
Source: Brigham and Women’s Hospital
Overview: College students have rapidly incorporated generative AI into daily life, and many are now using it for mental health support. In a study co-led by investigators at Mass General Brigham, 18% of respondents reported AI use for mental health, with heavier use among students reporting more severe symptoms.
The study appears in the Journal of Affective Disorders.
“Students 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. “Many students find these tools immediately useful, but we must understand where they help and where they create harm or delay needed human care.”
Liu and colleagues analyzed responses from the 2024–2025 Healthy Minds Study, an annual web-based survey about mental health experiences among U.S. college students. Among 675 students with complete data across two institutions, rates of AI use for mental health were higher among those with substantial symptom burden. The strongest predictor was general frequent AI use, but clinical indicators—moderate and severe depression, severe anxiety, and suicidality—each independently increased the odds of turning to AI for mental health.
The research also highlights demographic differences, notably increased AI-based mental health use among Asian students, and suggests that lifetime experience with therapy, but not necessarily current therapy, predicts greater AI use. Authors emphasize the need for institutions and technology companies to respond cooperatively.
Recommendations from the investigators include integrating robust crisis detection and referral pathways into AI platforms, training campus clinicians to ask about AI use, and designing low-barrier human alternatives that reduce the reliance on unregulated digital supports.
Authorship: In addition to Cindy H. Liu, Mass General Brigham authors include Wenbo Zhang, Felix Lou, and Chang Zhao. Additional contributors include Angela Chow and Tiffany Yip.
Disclosures: Liu serves as an advisor for youth mental health with Surgo Health, The Asian American Foundation, and a youth-focused project supported by The Manton Foundation. Other authors report no known competing financial interests or relationships.
Funding: None.
Key Questions Answered:
A: Generative AI removes many immediate barriers: scheduling delays, social stigma, and the emotional effort required to attend an appointment and disclose sensitive concerns to a stranger. For a student in crisis, an AI chatbot is instantly available at any hour, delivers nonjudgmental responses, and provides immediate validation, making it an attractive alternative when formal care feels inaccessible.
A: AI often functions as a reflective mirror rather than a genuine relational partner. Effective human therapy challenges clients to regulate emotions, tolerate corrective feedback, and develop perspective-taking through interaction with another person. If AI consistently offers unconditional validation, it can create a protective but ultimately limiting comfort zone that hinders the development of real-world coping skills and social problem-solving.
A: They should collaborate to embed strict safety measures and crisis-detection tools into AI platforms. Developers need to ensure their models can reliably recognize suicidal ideation or acute panic and trigger human crisis referrals. Universities should proactively ask students about AI use, expand accessible human services, and tailor outreach to groups more likely to rely on digital supports.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- The full journal paper was reviewed in preparing this summary.
- Additional context was provided by editorial staff to clarify implications for policy and practice.
About this AI and mental health research news
Author: Cassandra Falone
Source: Mass General Brigham
Contact: Siyun Qin – Mass General Brigham
Image: The image is credited to Neuroscience News
Original Research: Open access. Clinical and sociodemographic predictors of AI use for mental health among college students. Journal of Affective Disorders. DOI: 10.1016/j.jad.2026.122058
Abstract
Clinical and sociodemographic predictors of AI use for mental health among college students
Background
Generative AI tools have become widely accessible to college populations, yet patterns of use for mental health support remain underexplored. This study identified clinical and demographic predictors of AI use for mental health among students at two U.S. institutions.
Methods
Data came from students (n = 896) who completed an AI-focused module in the 2024–2025 Healthy Minds Study. The analytic sample included 675 students with complete data. Analyses compared groups who never used AI, used AI but not for mental health, and used AI for mental health. Hierarchical logistic regression examined predictors of AI use for mental health as a binary outcome, with supplementary multinomial models as described.
Results
About 18% of students reported using AI for mental health. The never-use group had higher proportions of non-binary/other gender and LGBQ+ students. Asian student representation increased across groups. Frequent general AI use was the strongest predictor. Moderate and severe depression, severe anxiety, and suicidality all approximately doubled the odds of using AI for mental health. Asian students showed elevated odds, and lifetime therapy history predicted AI use while current therapy did not.
Limitations
Data derive from only two institutions and are cross-sectional, limiting causal inference. Prevalence estimates may shift rapidly with ongoing AI adoption.
Conclusions
Students with serious mental health symptoms and certain marginalized groups are using unregulated AI tools at higher rates. The findings highlight the need for targeted research on AI safety for distressed individuals and for campus and technology policies that acknowledge who is using these tools and why.