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

2026-01-30

AI Summary

This study addresses the limited understanding of how individuals with mental health conditions engage with large language models (LLMs) for psychological support in real-world contexts. Through semi-structured interviews with 20 participants in the UK diagnosed with mental health disorders, and employing reflexive thematic analysis, the research uncovers a pattern wherein users actively establish boundaries around LLM use based on their prior therapeutic experiences. Findings indicate that LLMs are perceived as suitable for mild to moderate emotional distress but exhibit significant limitations in crisis intervention, trauma processing, and complex socio-emotional scenarios. The study underscores “boundary awareness” as a core principle for integrating AI-based psychological support into care ecosystems and provides empirical grounding for responsible design and governance of such systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: User Experience and Usability
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

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.

Problem

• mental health
• Large Language Models
• lived experience
• human-AI interaction
• digital mental health

Innovation

• conditional companion
• boundary-setting
• lived experience
• mental health support
• LLM governance