Digital overtourism in AI travel recommendations: evidence from a comparative analysis
Maryam Najafi, Carlos Manuel Martins da Costa · Current Issues in Tourism · 2026
While AI-based travel recommenders promise personalisation, their effects on destination concentration remain underexamined. This study presents a comparative analysis of 420 AI-generated travel recommendations produced by ten widely used AI systems in response to 14 structured tourism-related queries. Although AI systems reference numerous destinations, effective diversity remains limited, with outputs repeatedly converging on a narrow set of iconic and semi-iconic places. Sustainability-oriented prompts often reproduce symbolic ‘green’ narratives without meaningful spatial dispersion, while crisis- and culture-related queries privilege highly visible regions over peripheral destinations. The study conceptualises this pattern as digital overtourism – the algorithmic concentration of destination visibility at the informational, pre-travel stage. It further models digital overtourism as a recursive visibility amplification process rooted in uneven digital representation and popularity-weighted ranking structures, through which AI systems stabilise narrow destination hierarchies that may contribute to downstream demand pressure. Overall, the findings underscore the need for governance-sensitive and structurally recalibrated approaches to AI-driven tourism recommendation systems.