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

TL;DR

The paper investigates how people use large language model (LLM) chatbots for mental health support through 21 in-depth interviews with globally diverse participants. It introduces the concept of therapeutic alignment, analyzing how AI outputs align with established psychotherapy values and how users actively co-create meaningful interactions. Findings show chatbots fill care gaps and provide accessible, nonjudgmental spaces, but cultural/linguistic mismatches and boundary risks limit their therapeutic effectiveness. The authors offer design recommendations to build therapeutically aligned AI tools that balance user agency with safety, and advocate for localization and integration with traditional mental health care to maximize real-world impact.

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.