From Interaction to Attitude: Exploring the Impact of Human-AI Cooperation on Mental Illness Stigma
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917882896580608 |
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| author | Song, Tianqi Jamieson, Jack Zhu, Tianwen Yamashita, Naomi Lee, Yi-Chieh |
| author_facet | Song, Tianqi Jamieson, Jack Zhu, Tianwen Yamashita, Naomi Lee, Yi-Chieh |
| contents | AI conversational agents have demonstrated efficacy in social contact interventions for stigma reduction at a low cost. However, the underlying mechanisms of how interaction designs contribute to these effects remain unclear. This study investigates how participating in three human-chatbot interactions affects attitudes toward mental illness. We developed three chatbots capable of engaging in either one-way information dissemination from chatbot to a human or two-way cooperation where the chatbot and a human exchange thoughts and work together on a cooperation task. We then conducted a two-week mixed-methods study to investigate variations over time and across different group memberships. The results indicate that human-AI cooperation can effectively reduce stigma toward individuals with mental illness by fostering relationships between humans and AI through social contact. Additionally, compared to a one-way chatbot, interacting with a cooperative chatbot led participants to perceive it as more competent and likable, promoting greater empathy during the conversation. However, despite the success in reducing stigma, inconsistencies between the chatbot's role and the mental health context raised concerns. We discuss the implications of our findings for human-chatbot interaction designs aimed at changing human attitudes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01220 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Interaction to Attitude: Exploring the Impact of Human-AI Cooperation on Mental Illness Stigma Song, Tianqi Jamieson, Jack Zhu, Tianwen Yamashita, Naomi Lee, Yi-Chieh Human-Computer Interaction AI conversational agents have demonstrated efficacy in social contact interventions for stigma reduction at a low cost. However, the underlying mechanisms of how interaction designs contribute to these effects remain unclear. This study investigates how participating in three human-chatbot interactions affects attitudes toward mental illness. We developed three chatbots capable of engaging in either one-way information dissemination from chatbot to a human or two-way cooperation where the chatbot and a human exchange thoughts and work together on a cooperation task. We then conducted a two-week mixed-methods study to investigate variations over time and across different group memberships. The results indicate that human-AI cooperation can effectively reduce stigma toward individuals with mental illness by fostering relationships between humans and AI through social contact. Additionally, compared to a one-way chatbot, interacting with a cooperative chatbot led participants to perceive it as more competent and likable, promoting greater empathy during the conversation. However, despite the success in reducing stigma, inconsistencies between the chatbot's role and the mental health context raised concerns. We discuss the implications of our findings for human-chatbot interaction designs aimed at changing human attitudes. |
| title | From Interaction to Attitude: Exploring the Impact of Human-AI Cooperation on Mental Illness Stigma |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.01220 |