From Dyads to Groups: Rethinking Emotional Support with Conversational AI

2026-02-28

AI Summary

This study addresses a critical gap in current AI-based emotional support systems, which predominantly focus on one-on-one interactions between a single user and an individual AI agent, thereby overlooking the potential of group-based support. For the first time, this work introduces a multi-agent framework into the domain of AI emotional support, grounded in social support theory. The authors design and empirically validate a group support system composed of multiple AI agents through three integrated experiments combining multi-agent dialogue systems, experimental psychology methodologies, and user perception assessments. Results demonstrate that group-based AI significantly enhances users’ perceived support effectiveness, an effect mediated by users’ sense of connection with the system and moderated by the specific combination of support types provided. These findings elucidate the psychological mechanisms and configurational advantages of group AI support, offering a novel paradigm for next-generation emotional support systems.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Humans and AI: Emotional Intelligence
• 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
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Advances in artificial intelligence (AI), together with persistent gaps in access to reliable emotional support, have positioned AI as an increasingly prominent source of emotional assistance. However, most AI-based emotional support applications and prior research focus on one-on-one interactions between users and a single AI agent, leaving the potential advantages of alternative support configurations largely unexplored. Drawing on social support and support group theory, this research examines whether AI-based emotional support delivered by a group of AI agents (group AI support) can constitute a more effective support form than single-agent support (single AI support). We propose that group AI support enhances users' perceived support efficacy, that this effect operates by strengthening users' connectedness with the AI system, and that the composition of support types within AI groups further shapes support outcomes. Three experiments provide convergent support for these claims. By identifying when and why group AI emotional support outperforms single AI support, this work advances theoretical understanding of AI-based emotional support and provides actionable guidance for the design of AI support systems.

Problem

• emotional support
• conversational AI
• group AI
• social support
• AI agents

Innovation

• group AI support
• emotional support
• conversational AI
• social connectedness
• support composition