How Would You Like Your AI to Respond? A Preliminary Study of Emotional Preferences for Chatbot Support Across Life Scenarios

Abstract

While much research has focused on detecting user emotions, far less is known about how chatbots should express emotion back to users. This paper explores user preferences for chatbot emotional intensity across everyday situations. We conducted a mixed-methods study with 51 participants who evaluated chatbot responses at three emotional levels, non-emotional, moderate, and deep emotional, across twelve realistic scenarios, complemented by surveys and interviews. Results suggest that preferences are highly context-dependent: deep empathy was often valued, but moderation was preferred in certain scenarios. We did not observe robust gender effects in these preference patterns. Interviews further revealed ambivalence, as participants appreciated empathetic support but expressed concerns about authenticity, dependency, and fairness. We offer preliminary empirical insights, design considerations for context-aware emotional adaptivity, and ethical reflections on emotionally responsive AI.

Keywords: Chatbot, Emotional Response, Affective Computing, Human-AI Interaction

DOI: 10.54941/ahfe1007526