A Conditional Companion: Lived Experiences of People with Mental Health Disorders Using LLMs

TL;DR

This study explores how people with mental health conditions use LLMs for support, revealing their engagement patterns, perceived boundaries, and design considerations for safe integration into mental health care.

Contribution

It provides empirical insights into lived experiences with LLMs for mental health, emphasizing boundary-setting and proposing responsible design and governance strategies.

Findings

LLMs are used for immediacy and non-judgement support

Participants see LLMs as effective for mild-to-moderate distress

Boundaries are crucial for safe use of LLMs in mental health

Abstract

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.