AI on My Shoulder: Supporting Emotional Labor in Front-Office Roles with an LLM-based Empathetic Coworker
🤖 AI Summary
To address emotional labor overload and mental health risks faced by customer service representatives (CSRs) when handling impolite customers, this study introduces Care-Pilot—the first empathic AI collaborator designed to support frontline service workers through a closed-loop emotion regulation framework. Built upon fine-tuned large language models, Care-Pilot integrates context-aware empathic prompting with multi-turn human–AI co-creation to generate adaptive, supportive responses tailored to diverse incivility scenarios, thereby facilitating CSRs’ emotion regulation, cognitive reframing, and humanized client understanding. We propose the novel evaluation dimension “empathic operability,” demonstrating that AI-generated empathic responses significantly outperform human-written ones in perceived authenticity and actionability (665 pairwise comparisons; 143 CSR raters). A 20-participant simulation study further confirms that Care-Pilot effectively mitigates negative cognition and promotes psychological reset.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Emotional support for CSRs
LLM-based empathetic assistant
Mental well-being in client interactions
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-based empathetic coworker
Adaptive empathy in incivility
AI-mediated emotional labor support
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