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

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    Embodied virtual agents (EVAs) with large language models (LLMs) improve collaboration by enhancing social dynamics, not task efficiency. Agent embodiment boosts co-presence and user preference in triadic XR tasks.

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    Area of Science:

    • Human-Computer Interaction
    • Artificial Intelligence
    • Virtual Reality

    Background:

    • Triadic collaboration with conflicting goals presents unique challenges.
    • Mediating these collaborations often requires sophisticated AI.
    • Embodied Virtual Agents (EVAs) offer a potential solution for dynamic mediation.

    Purpose of the Study:

    • To investigate the impact of embodied vs. disembodied Large Language Model (LLM)-driven agents in mediating triadic collaboration.
    • To assess how agent presence and embodiment affect user experience, collaboration quality, and task outcomes.
    • To explore emergent mediation strategies of LLM-driven agents.

    Main Methods:

    • Developed and evaluated an Extended Reality (XR) system for office design tasks.
    • 24 pairs of users collaborated with LLM-driven agents.
    • Assessed impact of agent embodiment and presence on co-presence, collaboration, user preference, task efficiency, and satisfaction.
    • Qualitative analysis of agent mediation strategies.

    Main Results:

    • Agent embodiment significantly enhanced co-presence, perceived collaboration, and user preference.
    • Frequent interaction with disembodied agents correlated with lower user preference.
    • Agent presence alone did not improve task efficiency or satisfaction.
    • LLM-driven agents spontaneously exhibited facilitative and evaluative mediation strategies.

    Conclusions:

    • The value of EVAs in complex collaboration stems from shaping social dynamics and providing nuanced mediation.
    • Agent embodiment is crucial for fostering positive user experiences and collaboration.
    • LLM-driven agents show potential as adaptive collaborative aids without explicit programming.