The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support

People experiencing severe distress increasingly use Large Language Model (LLM) chatbots as mental health support tools. Discussions on social media have described how engagements were lifesaving for some, but evidence suggests that general-purpose LLM chatbots also have notable risks that could endanger the welfare of users if not designed responsibly. In this study, we investigate the lived experiences of people who have used LLM chatbots for mental health support. We build on interviews with 21 individuals from globally diverse backgrounds to analyze how users create unique support roles for their chatbots, fill in gaps in everyday care, and navigate associated cultural limitations when seeking support from chatbots. We ground our analysis in psychotherapy literature around effective support, and introduce the concept of therapeutic alignment, or aligning AI with therapeutic values for mental health contexts. Our study offers recommendations for how designers can approach the ethical and effective use of LLM chatbots and other AI mental health support tools in mental health care.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-10-16
DOI
https://doi.org/10.1145/3757430
Citations
9
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
13.90
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article

The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support

Sachin R. Pendse, Neha Kumar, Munmun De Choudhury, Inhwa Song
9 citations
Proceedings of the ACM on Human-Computer Interaction
Digital Mental Health Interventions
13.90
article

The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support

Sachin R. Pendse, Neha Kumar, Munmun De Choudhury, Inhwa Song
article en
9 citations

Abstract

People experiencing severe distress increasingly use Large Language Model (LLM) chatbots as mental health support tools. Discussions on social media have described how engagements were lifesaving for some, but evidence suggests that general-purpose LLM chatbots also have notable risks that could endanger the welfare of users if not designed responsibly. In this study, we investigate the lived experiences of people who have used LLM chatbots for mental health support. We build on interviews with 21 individuals from globally diverse backgrounds to analyze how users create unique support roles for their chatbots, fill in gaps in everyday care, and navigate associated cultural limitations when seeking support from chatbots. We ground our analysis in psychotherapy literature around effective support, and introduce the concept of therapeutic alignment, or aligning AI with therapeutic values for mental health contexts. Our study offers recommendations for how designers can approach the ethical and effective use of LLM chatbots and other AI mental health support tools in mental health care.

Proceedings of the ACM on Human-Computer InteractionVol. 9(7)
Georgia Institute of Technology (US), Korea Advanced Institute of Science and Technology (KR), University of California, San Francisco (US)
Openalex Percentile: Top 1%
Digital Mental Health Interventions
13.90
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The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support — Sachin R. Pendse, Neha Kumar, et al. · Proceedings of the ACM on Human-Computer Interaction (2025) | TGRS Research Map | TGRS