ESR-Coach: Leveraging Large Language Models for Training People to Provide Emotionally Supportive Responses

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Authors: Gongyao Jiang, Junze Li, Xiaojuan Ma, Qiong Luo
Cited by: 1
References: 65

Abstract

Effectively providing emotional support is a critical yet intricate interpersonal skill. Supporters often lack accessible and practical training opportunities to develop this competency. To address this gap, we introduce ESR-Coach, a Large Language Model (LLM)-based coaching system designed to train individuals in emotionally supportive communication. ESR-Coach leverages multiple AI agents to generate practice scenarios, demonstrate reference responses, and provide assessments on user practice replies. We evaluate the proficiency of our system on these three tasks, demonstrating high-fidelity case generation, helpful exemplary responses, and valid response assessments. In our user study (N=20), ESR-Coach helped participants achieve an average improvement of 17% in response helpfulness. After training, participants also employed more diverse and effective strategies. We further discuss the social intelligence of LLMs and their potential to foster humans’ interpersonal skills in real-world scenarios.

Fields of Study & Subjects

Mental Health via Writing Emotion and Mood Recognition Digital Mental Health Interventions

Publication details

Journal / source
Proceedings of the 31st International Conference on Intelligent User Interfaces
Publisher
ACM
Pages
1157–1177
Publication date
3 March 2026
Language
EN