Human-Machine Trust in "Cyber Guides": A User Study of Large Language Model-Based Intelligent Guides in the West Lake Museum Context
Abstract
As generative AI agents increasingly serve as cultural intermediaries in museums, empirical research on tourists' trust in these "cyber guides" remains scarce. Drawing on human-computer trust and parasocial interaction theories, this mixed-methods study (surveys and interviews) at the West Lake Museum investigates the dimensions, determinants, and behavioral outcomes of tourist trust in AI guides. Findings reveal that such trust is inherently selective. Alongside traditional dimensions——competence, integrity, and benevolence——affective trust is emerging. Furthermore, human-AI trust is fragile; a single negative interaction causes severe, costly-to-repair trust violations. This paper refines and expands the application of human-machine trust theory in cultural tourism contexts, offering concrete recommendations for optimizing AI-guided tour designs in museum institutions. It serves as a reference for enhancing the efficiency of cultural management and cultural services in the era of AI.
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