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

• URL: http://arxiv.org/abs/2602.00402v1
• Date: Fri, 30 Jan 2026 23:36:48 GMT
• Title: A Conditional Companion: Lived Experiences of People with Mental Health Disorders Using LLMs
• Authors: Aditya Kumar Purohit, Hendrik Heuer,
• Abstract summary: Large Language Models (LLMs) are increasingly used for mental health support.<n>We conducted 20 semi-structured interviews with people in the UK who live with mental health conditions.<n>We found that participants engaged with LLMs in conditional and situational ways.<n>LLMs were effective for mild-to-moderate distress but inadequate for crises, trauma, and complex social-emotional situations.
• Score: 14.106667217451887
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models [72.36715571932696]
  Narrative therapy helps individuals transform problematic life stories into empowering alternatives.<n>Current approaches lack realism in specialized psychotherapy and fail to capture therapeutic progression over time.<n>Int (Interactive Narrative Therapist) simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate expert-like responses.
  arXiv  Detail & Related papers  (2025-07-27T11:52:09Z)
• "It Listens Better Than My Therapist": Exploring Social Media Discourse on LLMs as Mental Health Tool [1.223779595809275]
  Large language models (LLMs) offer new capabilities in conversational fluency, empathy simulation, and availability.<n>This study explores how users engage with LLMs as mental health tools by analyzing over 10,000 TikTok comments.<n>Results show that nearly 20% of comments reflect personal use, with these users expressing overwhelmingly positive attitudes.
  arXiv  Detail & Related papers  (2025-04-14T17:37:32Z)
• Dr. GPT in Campus Counseling: Understanding Higher Education Students' Opinions on LLM-assisted Mental Health Services [12.190234761761923]
  Large Language Models (LLMs) can be leveraged to enhance students' mental well-being. Our findings revealed that students' acceptance of LLMs varied by scenario. These insights inform how AI technology should be designed and implemented to effectively support and enhance students' mental well-being.
  arXiv  Detail & Related papers  (2024-09-26T06:40:45Z)
• LLM Internal States Reveal Hallucination Risk Faced With a Query [62.29558761326031]
  Humans have a self-awareness process that allows us to recognize what we don't know when faced with queries. This paper investigates whether Large Language Models can estimate their own hallucination risk before response generation. By a probing estimator, we leverage LLM self-assessment, achieving an average hallucination estimation accuracy of 84.32% at run time.
  arXiv  Detail & Related papers  (2024-07-03T17:08:52Z)
• LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
  We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
  arXiv  Detail & Related papers  (2024-06-09T09:03:11Z)
• The opportunities and risks of large language models in mental health [3.9327284040785075]
  Global rates of mental health concerns are rising. There is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health.
  arXiv  Detail & Related papers  (2024-03-21T19:59:52Z)
• PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
  Psychological measurement is essential for mental health, self-understanding, and personal development. PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
  arXiv  Detail & Related papers  (2024-02-19T18:00:30Z)
• Large Language Model for Mental Health: A Systematic Review [2.9429776664692526]
  Large language models (LLMs) have attracted significant attention for potential applications in digital health. This systematic review focuses on their strengths and limitations in early screening, digital interventions, and clinical applications.
  arXiv  Detail & Related papers  (2024-02-19T17:58:41Z)
• Challenges of Large Language Models for Mental Health Counseling [4.604003661048267]
  The global mental health crisis is looming with a rapid increase in mental disorders, limited resources, and the social stigma of seeking treatment. The application of large language models (LLMs) in the mental health domain raises concerns regarding the accuracy, effectiveness, and reliability of the information provided. This paper investigates the major challenges associated with the development of LLMs for psychological counseling, including model hallucination, interpretability, bias, privacy, and clinical effectiveness.
  arXiv  Detail & Related papers  (2023-11-23T08:56:41Z)
• Rethinking Large Language Models in Mental Health Applications [42.21805311812548]
  Large Language Models (LLMs) have become valuable assets in mental health. This paper offers a perspective on using LLMs in mental health applications.
  arXiv  Detail & Related papers  (2023-11-19T08:40:01Z)
• Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns [51.622612759892775]
  Social cognitive theory explains how people learn and acquire knowledge through observing others. Recent years have witnessed the rapid development of large language models (LLMs) LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
  arXiv  Detail & Related papers  (2023-05-08T16:10:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.