AI on My Shoulder: Supporting Emotional Labor in Front-Office Roles with an LLM-based Empathetic Coworker

Picture of Qiuyue
Qiuyue "Joy" Zhong
Picture of Jash Rajesh Parekh
Jash Rajesh Parekh
Picture of Yechan Jeon
Yechan Jeon
Picture of Roy Zimmermann
Roy Zimmermann
Picture of Mary P Czerwinski
Mary P Czerwinski
Picture of Jina Suh
Jina Suh
Picture of Koustuv Saha
Koustuv Saha
Picture of Javier Hernandez
Javier Hernandez
Published at Proceedings of the Conference on Human Factors in Computing Systems (CHI) 2025

Abstract

Client-Service Representatives (CSRs) are vital to organizations. Frequent interactions with disgruntled clients, however, disrupt their mental well-being. To help CSRs regulate their emotions while interacting with uncivil clients, we designed Care-Pilot, an LLM-powered assistant, and evaluated its efficacy, perception, and use. Our comparative analyses between 665 human and Care-Pilot-generated support messages highlight Care-Pilot's ability to adapt to and demonstrate empathy in various incivility incidents. Additionally, 143 CSRs assessed Care-Pilot's empathy as more sincere and actionable than human messages. Finally, we interviewed 20 CSRs who interacted with Care-Pilot in a simulation exercise. They reported that Care-Pilot helped them avoid negative thinking, recenter thoughts, and humanize clients; showing potential for bridging gaps in coworker support. Yet, they also noted deployment challenges and emphasized the indispensability of shared experiences. We discuss future designs and societal implications of AI-mediated emotional labor, underscoring empathy as a critical function for AI assistants for worker mental health.

Materials