A Conditional Companion: Lived Experiences of People with Mental Health Disorders Using LLMs
This addresses the need for safe and effective integration of LLMs into mental health care, though it is incremental as it builds on existing research about technology in mental health.
The study investigated how people with mental health disorders use LLMs for support, finding they engage conditionally for benefits like immediacy and non-judgment, but set boundaries due to limitations in handling crises or complex situations.
Large Language Models (LLMs) are increasingly used for mental health support, yet little is known about how people with mental health challenges engage with them, how they evaluate their usefulness, and what design opportunities they envision. We conducted 20 semi-structured interviews with people in the UK who live with mental health conditions and have used LLMs for mental health support. Through reflexive thematic analysis, we found that participants engaged with LLMs in conditional and situational ways: for immediacy, the desire for non-judgement, self-paced disclosure, cognitive reframing, and relational engagement. Simultaneously, participants articulated clear boundaries informed by prior therapeutic experience: LLMs were effective for mild-to-moderate distress but inadequate for crises, trauma, and complex social-emotional situations. We contribute empirical insights into the lived use of LLMs for mental health, highlight boundary-setting as central to their safe role, and propose design and governance directions for embedding them responsibly within care ecosystem.