Generative AI for storytelling in cultural tourism: enhancing visitor engagement through AI-driven narratives - Saosing, Rungtiva; Nattawuttisit, Sooksawaddee (Edelweiss Applied Science and Technology, 2025)
This study develops and evaluates a generative AI-powered storytelling model designed to enhance cultural tourism by providing personalized, multilingual, and emotionally engaging visitor experiences. Grounded in a theoretical model integrating semiotic theories, human-centered AI design, and multimodal interaction, the model conceptualizes AI as a co-partner in culturally meaningful storytelling rather than merely a passive recommender. Addressing a critical gap in tourism technologies—the lack of adaptive, narrative-based interpretation tools—the study introduces a hybrid architecture integrating the GPT-4o model for dynamic storytelling, Retrieval-Augmented Generation (RAG) for context-sensitive recommendations, and a custom image-generation pipeline. A mobile application deploying this model was tested across four heritage sites in Bangkok with 400 international tourists from Thailand, China, Japan, Europe, and ASEAN. Model training occurred over 100 epochs using an 80/20 split, achieving an F1-score of 89.94%, classification accuracy of 87.39%, and semantic similarity scores of up to 0.95. Empirical findings indicate significant improvements in emotional engagement, cultural understanding, satisfaction, recommendation intentions, and memory retention. These findings reinforce the model’s efficacy, offering pragmatic guidelines for tourism authorities and cultural enterprises to modernize visitor services and appeal to global audiences through intelligent, adaptive storytelling.