Recomendaciones turísticas de la IA generativa: variaciones lingüísticas y culturales en las representaciones de destinos turísticos

Resumen

Este estudio analiza las variaciones lingüísticas y culturales en las recomendaciones turísticas generadas por modelos de lenguaje de gran tamaño (LLM), con foco en el comportamiento de ChatGPT ante consultas formuladas en español, euskera, inglés y ruso. Mediante un diseño experimental de métodos mixtos, se evaluaron 210 respuestas generadas a partir de prompts estructurados sobre tres destinos del País Vasco: Zumaia, Zarautz y Donostia-San Sebastián. Los resultados evidencian diferencias significativas en la precisión, exhaustividad y la estructura de las recomendaciones, dependiendo del idioma de consulta y la forma toponímica. Se observaron mayores tasas de errores y respuestas genéricas en idiomas extranjeros, especialmente en ruso, mientras que los idiomas locales ofrecieron recomendaciones más detalladas, aunque con ciertos riesgos de inexactitud factual. El reconocimiento de destinos se mostró sensible a la escritura, siendo menor en sistemas no latinos. Los resultados reflejan una disparidad lingüística y cultural en la generación automatizada de contenido turístico, probablemente vinculada a sesgos en los datos de entrenamiento. Se concluye que los sistemas de IA generativa deben adaptarse al contexto multilingüe del turismo, mediante estrategias colaborativas que integren desarrolladores, gestores turísticos y expertos culturales, para asegurar representaciones digitales precisas, inclusivas y culturalmente contextualizadas.

Palabras clave:

inteligencia artificial generativa, modelos de lenguaje de gran tamaño, destinos turísticos, recomendaciones turísticas, multilingüismo, toponimia

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