Practicing with Language Models Cultivates Human Empathic Communication
Aakriti Kumar, Nalin Poungpeth, Diyi Yang, Bruce Lambert, Matthew Groh

TL;DR
This study demonstrates that practicing with language models enhances human empathic communication skills, with personalized AI feedback significantly improving empathy expression in naturalistic conversations.
Contribution
The paper introduces a scalable AI-based coaching intervention that improves human empathic communication by providing personalized feedback based on a new taxonomy of empathic expressions.
Findings
Personalized AI feedback boosts empathic communication alignment.
People often feel empathy but fail to express it systematically.
Participants can reliably identify empathic responses as more expressive.
Abstract
Empathy is central to human connection, yet people often struggle to express it effectively. In blinded evaluations, large language models (LLMs) generate responses that are often judged more empathic than human-written ones. Yet when a response is attributed to AI, recipients feel less heard and validated than when comparable responses are attributed to a human. To probe and address this gap in empathic communication skill, we built Lend an Ear, an experimental conversation platform in which participants are asked to offer empathic support to an LLM role-playing personal and workplace troubles. From 33,938 messages spanning 2,904 text-based conversations between 968 participants and their LLM conversational partners, we derive a data-driven taxonomy of idiomatic empathic expressions in naturalistic dialogue. Based on a pre-registered randomized experiment, we present evidence that a…
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Taxonomy
TopicsDigital Mental Health Interventions · Action Observation and Synchronization · Empathy and Medical Education
