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HAT Swapping: Virtual Agents as Stand-Ins for Absent Human Instructors in Virtual Training
Embodied Virtual Agents (EVAs) in virtual reality training improve task efficiency. Matching EVA appearance to instructors enhances trust but risks uncanny effects, while unique appearances offer clarity but may reduce coherence.
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.
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