GroupEnvoy: A Conversational Agent Speaking for the Outgroup to Foster Intergroup Relations

2026-04-17

AI Summary

This study addresses how psychological and linguistic barriers often impede intergroup interaction and hinder improvements in intergroup relations. Grounded in intergroup contact theory, it introduces GroupEnvoy—an innovative dialogue agent that generates outgroup perspectives from real intergroup conversation transcripts and embeds them into ingroup collaborative discussions, thereby realizing, for the first time, AI-mediated intergroup contact through an outgroup representative. A mixed-methods experiment demonstrates that, compared to a text-reading control condition, engaging with GroupEnvoy significantly reduces intergroup anxiety and enhances perspective-taking. Notably, active AI mediation strengthens outcome expectations, whereas passive exposure more effectively fosters willingness for future contact, revealing distinct mechanisms through which different intervention modalities elicit empathy.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Conversational agents have the potential to support intergroup relations when psychological or linguistic barriers prevent direct interaction. Based on intergroup contact theory, we propose GroupEnvoy, a conversational agent that represents outgroup perspectives during ingroup discussions, grounded in transcripts from outgroup-only sessions. To evaluate this approach and derive design principles, we conducted a mixed-methods, between-subjects study with university students, where host-country students formed the ingroup and international students formed the outgroup. Ingroup students performed a collaborative task, receiving outgroup perspectives via GroupEnvoy (experimental) or reading written transcripts (control). Compared to the control group, the experimental group showed greater reduction in intergroup anxiety and greater improvement in perspective-taking. Qualitatively, AI-mediated contact enhanced outcome expectancies, whereas passive exposure fostered future contact intentions. The two conditions also elicited empathy toward distinct targets: outgroup evaluations of the ingroup versus outgroup lived experiences. These findings validate AI-mediated contact as a promising paradigm for improving intergroup relations.

Problem

• intergroup relations
• conversational agents
• intergroup contact
• outgroup perspectives
• AI-mediated contact

Innovation

• AI-mediated contact
• conversational agent
• intergroup relations
• outgroup representation
• perspective-taking