Exploring Mediation by an Embodied Virtual Agent in Immersive Triadic Collaborative Decision-Making

This study investigates Embodied Virtual Agents (EVAs) driven by Large Language Models (LLMs) as mediators in triadic collaboration where two users with conflicting goals must work towards a shared objective. We developed and evaluated an XR system where pairs (n = 24) collaborated on an office design task, assessing the impact of agent presence and embodiment. Our findings reveal that agent embodiment significantly enhanced co-presence, which in turn fostered interactions that led to better perceived collaboration and higher user preference, whereas frequent interactions with the disembodied agent was associated with lower user preference. Conversely, agent presence did not improve task efficiency or satisfaction. Notably, our qualitative analysis revealed that the LLM-driven agent spontaneously adopted emergent facilitative and evaluative mediation strategies that align with collaborating and compromising conflict-resolving modes, showcasing its potential as an adaptive collaborative aid without explicit programming. These results highlight that the value of EVAs in complex collaboration lies in their ability to shape social dynamics and provide nuanced, context-aware mediation, a different form of value than traditional productivity enhancement.