The work aims to explore the key demand factors of artificial intelligence generated content (AIGC) in tourist experience from the symbiotic state perspective and investigate design strategies for optimizing the experience. Using grounded theory, the study analyzed tourist experience demands from the symbiotic state perspective and integrated AIGC to explore their role in improving the experience. The Kano model was applied to classify core AIGC elements, analyze tourist preferences and satisfaction, and identify key demand factors in tourist experience. On this basis, a weight ranking was carried out to clarify the specific functions of each demand factor. The results suggest that AIGC should focus on personalized experiences, convenience, social interaction, cultural immersion, cost-effectiveness, and safety, with the most important applications being interactive itinerary planning, real-time social matching, personalized cultural interpretation, cost-effective recommendations, and safety assurance. Meeting these key demand factors could effectively enhance the overall travel experience of tourists. This study analyzes the functional attributes of AIGC on tourist experience from the symbiotic state perspective, summarizes the core functions of different demand categories, and further explores the role of AIGC in tourism experience optimization, offering a reference for the application of artificial intelligence in tourism service product design.
Key words
artificial intelligence generated content (AIGC) /
tourism services /
tourist experience /
design strategy
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