Mediated Social Support for Distress Reduction: AI Chatbots vs. Human

Abstract

The emerging uptake of AI chatbots for social support entails systematic comparisons between human and non-human entities as sources of support. In a between-subject experimental study, a human and two types of ostensible chatbots (using a wizard of oz design) had supportive conversations with college students who were experiencing stressful situations during the pandemic. We found that when compared with a less ideal chatbot (i.e., low-contingent chatbot), (1) the human support provider was perceived with more warmth, which directly reduced emotional distress among participants; (2) the ideal chatbot (i.e., high-contingent chatbot) was perceived to be more competent, which activated participants' cognitive reappraisal of their stressful situations and subsequently reduced emotional distress. The human provider and the ideal chatbot did not differ in users' perceived competence or warmth, although the human provider was more effective at activating participants' cognitive reappraisal. This study integrates human communication theories into human-computer interaction work and contributes by positioning and theorizing user perceptions of chatbots in a larger process from support sources with varying communication competence to users' cognitive and emotional responses, and ultimately to the stress outcome. Theoretical and design implications are discussed.

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Index Terms

• Human-centered computing

  • Human computer interaction (HCI)

    • HCI design and evaluation methods