AI on My Shoulder: Supporting Emotional Labor in Front-Office Roles with an LLM-based Empathetic Coworker
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Abstract (arXiv)
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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 21 of its 149 references that are in the map. First posted on arXiv 18 Oct 2024.
Builds on
21 of 149 references.
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9 more
- 2024Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chatbots for Public Health Interventioncited by 15
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Citing papers per year: 2025: 3; 2026: 8.
11 papers.
- 2025When Large Language Models are Reliable for Judging Empathic Communicationcited by 21
- 2025Balancing Caregiving and Self-Care: Exploring Mental Health Needs of Alzheimer's and Dementia Caregiverscited by 7
- 2025Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queriescited by 6
- 2026From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms for Dignified Human-AI Interactioncited by 6
- 2026AI generates well-liked but templatic empathic responsescited by 3
- 2026Designing KRIYA: An AI Companion for Wellbeing Self-Reflectioncited by 1
- 2026A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbotscited by 1
- 2026Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modelingcited by 0
- 2026TUX: Measuring Human--AI Tacit Understandingcited by 0
- 2026When AI Says "I have been in similar situations": Synthetic Lived Experience in Peer-Like Caregiver Supportcited by 0
- 2026Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptionscited by 0
- 2026Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptions15 Sep 2026
- 2026When AI Says "I have been in similar situations": Synthetic Lived Experience in Peer-Like Caregiver Support16 Jun 2026
- 2026TUX: Measuring Human--AI Tacit Understanding29 May 2026
- 2026Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modeling15 May 2026
- 2026AI generates well-liked but templatic empathic responses9 Apr 2026
- 2026From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms for Dignified Human-AI Interaction29 Jan 2026
- 2026A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbots21 Jan 2026
- 2026Designing KRIYA: An AI Companion for Wellbeing Self-Reflection21 Jan 2026
- 2025Balancing Caregiving and Self-Care: Exploring Mental Health Needs of Alzheimer's and Dementia Caregivers17 Jun 2025
- 2025When Large Language Models are Reliable for Judging Empathic Communication11 Jun 2025
- 2025Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries12 Apr 2025