A Conditional Companion: Lived Experiences of People with Mental Health Disorders Using LLMs: Conditional Companion: LLMs & Mental Health
Aditya Kumar Purohit, Hendrik Heuer
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 8945a693-c302-465e-84ff-3b82c7c9de3cBuilds on10
- Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text DataXuhai Xu, Bingsheng Yao, Yuanzhe Dong, Saadia Gabriel et al.UbiComp 2024 · 281 citations
- Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future DirectionsMagdalena Wischnewski, Nicole C. Krämer, Emmanuel MüllerCHI 2023 · 135 citations
- Unmet Needs and Opportunities for Mobile Translation AIDaniel J. Liebling, Michal Lahav, Abigail Evans, Aaron Donsbach et al.CHI 2020 · 47 citations
- The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health SupportInhwa Song, Sachin R. Pendse, Neha Kumar, Munmun De ChoudhuryCSCW 2025 · 41 citations
- A Longitudinal Goal Setting Model for Addressing Complex Personal Problems in Mental HealthElena Agapie, Patricia A. Areán, Gary Hsieh, Sean A. MunsonCSCW 2022 · 36 citations
Related papers
- More than Decision Support: Exploring Patients' Longitudinal Usage of Large Language Models in Real-World Healthcare-Seeking JourneysYancheng Cao, Yishu Ji, Chris Yue Fu, Sahiti Dharmavaram et al.CHI 2026 · 2 citations
- Reimagining Support: Exploring Autistic Individuals' Visions for AI in Coping with Negative Self-TalkBuse Çarik, Victoria V. Izaac, Xiaohan Ding, Angela Scarpa et al.CHI 2025 · 19 citations
- MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' JournalingTaewan Kim, Seolyeong Bae, Hyun Ah Kim, Su-Woo Lee et al.CHI 2024 · 112 citations
- 'I've talked to ChatGPT about my issues last night.': Examining Mental Health Conversations with Large Language Models through Reddit AnalysisKyuha Jung, Gyuho Lee, Yuanhui Huang, Yunan ChenCSCW 2025 · 23 citations
- "Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported InteractionsKellie Yu Hui Sim, Roy Ka-Wei Lee, Kenny Tsu Wei ChooCSCW 2026
