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    Area of Science:

    • Human-Computer Interaction
    • Virtual Reality Education
    • Artificial Intelligence in Learning

    Background:

    • Virtual reality (VR) offers immersive collaborative learning environments.
    • Integrating Artificial Intelligence (AI) tutors alongside human instructors requires understanding interaction dynamics.
    • The role and appearance of Embodied Virtual Agents (EVAs) substituting human instructors in Human-Agent Teaming (HAT) is underexplored.

    Purpose of the Study:

    • To investigate the impact of EVA appearance consistency on collaboration efficiency, user perception, and HAT dynamics in virtual training.
    • To compare the effects of shared-appearance (SA) and unique-appearance (UA) EVAs against a no-agent condition.
    • To provide design recommendations for mixed-agency interfaces in dynamic HAT systems.

    Main Methods:

    • Recruited 21 participants for a virtual training study.
    • Compared three conditions: No Agent, Shared-Appearance (SA) EVA, and Unique-Appearance (UA) EVA.
    • Evaluated collaboration efficiency, user perception/preference, and HAT dynamics.

    Main Results:

    • EVAs significantly enhanced task efficiency compared to no support.
    • Shared-Appearance (SA) EVAs fostered perceived continuity and trust but risked ambiguity and uncanny effects.
    • Unique-Appearance (UA) EVAs provided transparency and role clarity but could disrupt experiential coherence.

    Conclusions:

    • EVA appearance is a critical design consideration in HAT systems.
    • A trade-off exists between perceived continuity/trust (SA) and role clarity/transparency (UA).
    • Design choices for EVA appearance should balance user experience, trust, and system clarity in mixed-agency environments.