From Dyads to Groups: Rethinking Emotional Support with Conversational AI

TL;DR

This research explores how AI support groups, consisting of multiple AI agents, can provide more effective emotional support than single-agent AI, by enhancing user connectedness and support perception, supported by three experiments.

Contribution

It introduces the concept of group AI support for emotional assistance, demonstrating its advantages over single AI support through empirical experiments and theoretical insights.

Findings

Group AI support improves perceived support efficacy.

Connectedness with AI enhances support outcomes.

Support composition influences effectiveness.

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.