Paper Type
ERF
Abstract
AI based conversational agents are increasingly used to provide accessible and immediate support for non crisis mental health concerns. However, willingness to rely on AI for emotionally sensitive issues depends on perceived severity, empathy, privacy, safety, and trust. This study uses a vignette based Design Science Research approach to examine how university students evaluate AI chatbots compared with human counselors across realistic situations. Twenty three students responded to six vignettes, generating 138 open ended responses analyzed through open, axial, and selective coding. Findings reveal five patterns: conditional openness to AI, the importance of emotional understanding, tensions between privacy and safety, a threshold where higher severity issues require human support, and a preference for combining AI with human services. The study proposes design principles emphasizing clear communication of the chatbot’s supportive role, transparent data practices, severity responsive interactions, empathy aligned communication, strong privacy protections, professional oversight, and integration with human care pathways.
Paper Number
1894
Recommended Citation
Dembla, Pamila and Batra, Gunjan, "AI for mental health: A Design Science Approach" (2026). AMCIS 2026 Proceedings. 10.
https://aisel.aisnet.org/amcis2026/ai_aiaa/ai_aiaa/10
AI for mental health: A Design Science Approach
AI based conversational agents are increasingly used to provide accessible and immediate support for non crisis mental health concerns. However, willingness to rely on AI for emotionally sensitive issues depends on perceived severity, empathy, privacy, safety, and trust. This study uses a vignette based Design Science Research approach to examine how university students evaluate AI chatbots compared with human counselors across realistic situations. Twenty three students responded to six vignettes, generating 138 open ended responses analyzed through open, axial, and selective coding. Findings reveal five patterns: conditional openness to AI, the importance of emotional understanding, tensions between privacy and safety, a threshold where higher severity issues require human support, and a preference for combining AI with human services. The study proposes design principles emphasizing clear communication of the chatbot’s supportive role, transparent data practices, severity responsive interactions, empathy aligned communication, strong privacy protections, professional oversight, and integration with human care pathways.
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