“This is human intelligence debugging artificial intelligence”: Examining how people prompt GPT in seeking mental health support

Large language models (LLMs) could extend AI support for mental well-being with their unprecedented language understanding and generation ability. While we have seen individuals who lack access to professional care utilizing LLMs for mental health support, it is unclear how they prompt and interact with LLMs given their individualized emotional needs and life situations. In this work, we analyzed 49 threads and 7,538 comments on Reddit, aiming to understand how people seek mental health support from GPT by creating and crafting various prompts. Despite GPT explicitly disclaiming that it is not an alternative to professional care, we found that users continued to use it for support and devised different prompts to bypass the safety guardrails. Meanwhile, users actively refined and shared their prompts to make GPT more human-like by specifying nuanced communication styles and cultivating in-depth discussions. They also came up with several strategies to make GPT communicate more efficiently to enrich the customized personas on the fly or gain multiple perspectives. Reflecting on these findings, we discuss the tensions associated with using LLMs for mental health support and the implications for designing safer and more empowering human-LLM interactions. • We qualitatively analyzed social media threads on how people seek mental health support from GPT. • Despite the safety guardrails and disclaimers, users continued to use GPT as a care resource. • Users actively crafted prompts to make GPT appear humanlike and communicate efficiently. • Our results informed the design of safe and empowering human-LLM interaction for mental well-being.

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Publication Details

Journal
International Journal of Human-Computer Studies
Published
2025-06-09
DOI
https://doi.org/10.1016/j.ijhcs.2025.103555
Citations
4
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
6.18

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article

“This is human intelligence debugging artificial intelligence”: Examining how people prompt GPT in seeking mental health support

Yuhan Luo, Xinning Gui, Zhuoyang Li, Zihao Zhu
4 citations
International Journal of Human-Computer Studies
Digital Mental Health Interventions
6.18
article

“This is human intelligence debugging artificial intelligence”: Examining how people prompt GPT in seeking mental health support

Yuhan Luo, Xinning Gui, Zhuoyang Li, Zihao Zhu
article en
4 citations

Abstract

Large language models (LLMs) could extend AI support for mental well-being with their unprecedented language understanding and generation ability. While we have seen individuals who lack access to professional care utilizing LLMs for mental health support, it is unclear how they prompt and interact with LLMs given their individualized emotional needs and life situations. In this work, we analyzed 49 threads and 7,538 comments on Reddit, aiming to understand how people seek mental health support from GPT by creating and crafting various prompts. Despite GPT explicitly disclaiming that it is not an alternative to professional care, we found that users continued to use it for support and devised different prompts to bypass the safety guardrails. Meanwhile, users actively refined and shared their prompts to make GPT more human-like by specifying nuanced communication styles and cultivating in-depth discussions. They also came up with several strategies to make GPT communicate more efficiently to enrich the customized personas on the fly or gain multiple perspectives. Reflecting on these findings, we discuss the tensions associated with using LLMs for mental health support and the implications for designing safer and more empowering human-LLM interactions. • We qualitatively analyzed social media threads on how people seek mental health support from GPT. • Despite the safety guardrails and disclaimers, users continued to use GPT as a care resource. • Users actively crafted prompts to make GPT appear humanlike and communicate efficiently. • Our results informed the design of safe and empowering human-LLM interaction for mental well-being.

International Journal of Human-Computer StudiesVol. 203
Pennsylvania State University (US), City University of Hong Kong (HK)
City University of Hong Kong
Openalex Percentile: Top 4%
Digital Mental Health Interventions
6.18
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