Designing Effective Empathy in AI Agents: An Empirical Study of User-Centric vs. Situation-Centric Approaches between Human and AI Agents

Abstract

As generative AI systems play an increasing role in emotional support, scholars have raised concerns about discomfort, inauthenticity, and expectancy violations resulting from AI's empathic responses. Drawing on verbal person-centeredness theory, we propose User Centric Empathy (UCE: emotion-focused and validating) and Situation Centric Empathy (SCE: context-focused and redirecting) to identify a more effective AI empathy approach. Across two experiments, we investigate how empathy type (UCE vs. SCE) and agent type (human vs. AI) interact to shape user experience. The results indicate that, when expressed by an AI agent, situation-centric empathy (SCE) emerges as a more appropriate empathy strategy, as it reduces discomfort and inauthenticity. Interestingly, when blame is attributed to one’s own error rather than to external sources, the type of empathy expressed by the AI agent exerts no significant effect. These results highlight that the effectiveness of AI-delivered empathy depends less on mimicking human-like responses and more on adopting an appropriate empathy approach, showing that superficial mimicry cannot foster authentic relational outcomes.