AI in tourism and hospitality: from predictive infrastructure to experiential interface
Fei Hao, Xuejie Qiu, Haiyan Kong, Kangping Wang, Shuxu LiuPurpose
This study aims to review the evolution of artificial intelligence (AI) research in tourism and hospitality (T&H), tracing its shift from back-end analytical infrastructure to front-stage experiential interface. This study maps conceptual foundations, thematic dynamics and methodological patterns, with particular attention to generative AI (GenAI), and develops a future research agenda for responsible and sustainability-oriented AI innovation.
Design/methodology/approach
This study conducted a longitudinal bibliometric and thematic review of 1, 851 peer-reviewed articles (1986–2025) from Scopus and Web of Science Core Collection. Following the PRISMA guidelines, VOSviewer and Gephi were used for co-occurrence and citation mapping, interpreting thematic evolution across four historical phases to inform a theory-driven research agenda.
Findings
Two trajectories structure the field: AI as analytical infrastructure (optimizing forecasting and decision-making) and AI as experiential interface (facilitating generative, interactive and emotionally embedded service encounters). The field has evolved from symbolic logic and data mining to multimodal analytics, service robots and GenAI applications. The proposed agenda highlights methodological transparency, ethical and accountable AI, human–AI collaboration, employee welfare and sustainable transformation.
Originality/value
This study offers a structured, theory-informed synthesis of AI research in T&H. By linking historical trajectories with emerging priorities, it helps scholars, practitioners and policymakers understand where AI creates value, where risks arise and how future T&H research can respond to the GenAI era.