Generative AI tourism recommendations: linguistic and cultural variations in the representations of tourist destinations

Abstract

This study analyses linguistic and cultural variations in tourism recommendations generated by large language models (LLMs), focusing on the behaviour of ChatGPT when responding to prompts in Spanish, Basque, English, and Russian. Using a mixed-methods experimental design, 210 AI-generated responses were evaluated based on structured prompts related to three destinations in the Basque Country: Zumaia, Zarautz, and Donostia-San Sebastián. The results reveal significant differences in the accuracy, comprehensiveness, and structure of recommendations depending on the language of the query and the toponymic form used. Higher rates of errors and generic content were observed in foreign languages, particularly Russian, while local languages yielded more detailed recommendations, though with occasional factual inaccuracies. Destination recognition proved sensitive to the writing system, with reduced performance for non-Latin scripts. These findings reflect a linguistic and cultural disparity in the automated generation of tourism content, likely linked to biases in training data. The study concludes that generative AI systems must be adapted to the multilingual context of tourism through collaborative strategies involving developers, tourism managers, and cultural experts to ensure digital representations that are accurate, inclusive, and culturally contextualised.

Keywords:

generative artificial intelligence, large language models, tourism destinations, tourism recommendations,, multilingualism, toponymy

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