Assumptions and Undeclared Selection Criteria: The Usefulness of Generative AI as a Travel Recommender System

This paper examines the trustworthiness of generative AI as a tourism recommender system by analyzing how ChatGPT5.2 responds to an open-ended, zero-shot prompt: “Recommend me a list of 10 German Christmas Markets.” Using German Christmas markets as a case study, outputs, texts in reasoning panels, and cited sources of fifteen replicates (carried out over five consecutive days) were systematically documented and analyzed. The results show a consistent and patterned selection which is dominated by a small canon of markets (Nürnberg, Dresden, Köln, München, and Stuttgart). The generative AI model does not neutrally sample from the entire pool of approximately 2000 German markets but instead reproduces a narrow canon of “iconic” destinations. Analysis of reasoning traces and follow-up conversations demonstrates that ChatGPT5.2 applies hidden selection criteria, including canonical status, landmark setting, branding strength, and perceived trip-planning usefulness, while also introducing undisclosed filters such as geographic spread across Germany and stylistic diversity. Although the model claims to use source triangulation and quality checks, the evidence shows substantial reliance on tourism marketing pages, travel media, blogs, and social media, especially for descriptive commentary. The study concludes that generative AI tourism recommendations are useful but non-neutral and should be interpreted as “curated,” bias-bearing constructs rather than transparent information retrieval. The implications of this on tourism management and the marketing of Christmas markets are discussed.