ace“It happened to be the perfect thing”: experiences of generative AI chatbots for mental health
The global mental health crisis underscores the need for accessible, effective interventions. Chatbots based on generative artificial intelligence (AI), like ChatGPT, are emerging as novel solutions, but research on real-life usage is limited. We interviewed nineteen individuals about their experiences using generative AI chatbots for mental health. Participants reported high engagement and positive impacts, including better relationships and healing from trauma and loss. We developed four themes: (1) a sense of…
Interviewed users reported high engagement with generative AI chatbots like ChatGPT and described positive mental health impacts including improved relationships and healing from trauma and loss.
Participants emphasized that generative AI chatbots need better safety guardrails and further research is needed on safety and effectiveness for mental health use.
Findings are based on a small qualitative sample of nineteen individuals and real-life usage research is still limited, with safety and effectiveness not yet established.
Evidence
- Peer-reviewedUniversal Access in the Information Society2026-01-06
- Peer-reviewednpj Mental Health Research2024-10-27
- Peer-reviewedJMIR Mental Health2026-02-26
- Peer-reviewedProceedings of the ACM on Human-Computer Interaction2025-10-16
- Peer-reviewedProceedings of the ACM on Human-Computer Interaction2025-10-16
How should this claim be treated?
Truvace Impact Record TRV-2026-0353, v5: ““It happened to be the perfect thing”: experiences of generative AI chatbots for mental health.” Truvace, 2026-09-19. /record/TRV-2026-0353 (accessed at citation time). sha256 b18e2d6278d3f60e…
Calibration history
Every change to this record since certification, in the open.
Source set updated
Source set updated
Source set updated
Source set updated
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0353 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.