ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Digital Mental Health
Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1634714
This article is part of the Research TopicAdvances in Generative Artificial Intelligence for Mental HealthView all articles
Development and Evaluation of LLM-Based Suicide Intervention Chatbot
Provisionally accepted
- 1Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing, China
- 2University of the Chinese Academy of Sciences, Beijing, China
- 3The Fifth People's Hospital of Nanning, Nanning, China
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Suicide accounts for over 720,000 deaths globally each year, and many more individuals experiencing suicidal ideation; thus, implementing large-scale, effective suicide intervention is vital for reducing suicidal behaviors. Traditional suicide intervention methods are hampered by shortages of qualified practitioners, variability in clinical competence, and high service costs. This study leverages Large Language Models (LLMs) to develop an effective suicide intervention chatbot, which provides early, large-scale, rapid self-help interventions. First, according to existing psychological crisis intervention methods, we fine-tuned ChatGPT-4 via prompt engineering to develop a chatbot that promptly responds to the needs of individuals experiencing suicidal ideation. Then, we implemented a self-help web-based dialogue platform powered by this chatbot and conducted the evaluations of its usability and intervention efficacy. We found that the self-help suicide intervention chatbot achieved high effectiveness and quality in terms of user interface operability, interaction experience, emotional support, intervention efficacy, safety and privacy, and overall satisfaction. These findings demonstrate that the suicide intervention chatbot can provide effective emotional support and therapeutic intervention to a large cohort experiencing suicidal ideation.
Keywords: Suicidal Ideation, Large Language Model, Chatbot, self-help psychological crisis intervention, Suicide prevention and intervention
Received: 25 May 2025; Accepted: 10 Jul 2025.
Copyright: © 2025 Cui, Gu, Fang and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hui Fang, The Fifth People's Hospital of Nanning, Nanning, China
Tingshao Zhu, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing, China
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