Laboratory study explores how user personality traits influence interaction quality with AI agents, suggesting deeper design considerations.
As embodied conversational agents (ECAs) reach unprecedented visual photorealism, aligning their communicative behaviors with human social norms becomes a critical challenge for interaction design. This laboratory study N=40 investigates how agent behaviors—specifically gaze patterns, interruption-handling strategies, and expressed personas—interact with user personality traits to shape interaction quality. Utilizing a high-fidelity MetaHuman agent within a Wizard-of-Oz framework, we conducted a multi-modal analysis integrating subjective measures, qualitative interviews, and facial Action Unit (AU) analysis. Our findings reveal that user personality traits, particularly Agreeableness and Conscientiousness, significantly moderate social expectation: while high-agreeableness users exhibit greater "social forgiveness" toward norm violations, high-conscientiousness users prioritize interactional efficiency and are highly sensitive to conversational overlaps. Crucially, we identify a phenomenon of "hidden social friction," where high-fidelity agents compel users to perform "emotional labor" manifesting as masking smiles (AU09 + AU12) during disruptive interruptions. We argue that intelligent agent design should move beyond superficial anthropomorphism toward a "normative alignment" that accounts for individual psychological differences. This work contributes to the CSCW community by providing a nuanced understanding of interactional costs associated with digital human photorealism and offering heuristics for developing more socially resilient and personality-aware autonomous systems.
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Ye et al. (2026) studied this question.
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