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Empathy in action: An empirical exploration of user perspectives on conversational agent empathy

Bumho Lee et al.

International Journal of Human-Computer Studies2026https://doi.org/10.1016/j.ijhcs.2026.103797article
AJG 2ABDC B
Weight
0.50

Abstract

• A holistic framework of conversational agent (CA) empathy is developed and validated, integrating user insights with established empathy theories. • Open-ended surveys reveal core empathic behaviors in CAs, leading to the identification of four key agent traits: Active Listening, Personalization, Emotional Expressivity, and Persona Attractiveness. • The Interpersonal Reactivity Index (IRI) is applied to examine the influence of these traits on user perceptions of cognitive and affective empathy components. • Structural equation modeling confirms the impact of each trait on user evaluations of perspective-taking, fantasy, empathic concerns, and personal distress, providing guidance for designing empathetic CAs. As Conversational Agents (CAs) increasingly interact with users on social and emotional levels, understanding how these agents convey empathy has become a critical challenge. This paper reports on an exploratory mixed-methods study that propose and empirically explores an initial framework for CA empathic responsiveness. The research proceeds in two sequential studies. Study 1 first conducted a qualitative analysis of open-ended surveys (N=166, U.S. and South Korea) to identify key user-defined empathic behaviors. Through thematic analysis, these insights were integrated with existing empathy theories to derive a framework of four core agent characteristics: Active Listening (AL), Personalization (PE), Emotional Expressivity (EE), and Persona Attractiveness (PA). Study 2 then conducted a quantitative investigation in South Korea (N=200) using Structural Equation Modeling (SEM) to test this framework. Empathic responsiveness was operationalized adopting Agent Empathic Reactivity Index (AERI), a validated CA-specific adaptation of the Interpersonal Reactivity Index (IRI), assessing perspective-taking, fantasy, empathic concerns, and personal distress. SEM results confirmed all 12 hypothesized paths. AL and PE strongly enhanced perspective-taking and empathic concerns, while PE also significantly reduced personal distress. Notably, EE and PA had dual effects: they improved positive dimensions, such as fantasy, but also significantly increased users’ perception of the agent’s personal distress. These findings highlight the delicate balance required in designing emotionally resonant CAs. This work advances the theoretical understanding of multidimensional agent empathy and provides actionable, nuanced guidance for designers aiming to build trust and foster long-term user relationships.

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https://doi.org/https://doi.org/10.1016/j.ijhcs.2026.103797

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@article{bumho2026,
  title        = {{Empathy in action: An empirical exploration of user perspectives on conversational agent empathy}},
  author       = {Bumho Lee et al.},
  journal      = {International Journal of Human-Computer Studies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.ijhcs.2026.103797},
}

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Empathy in action: An empirical exploration of user perspectives on conversational agent empathy

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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