How AI Shapes the Cultural Tourism Visitor Experience: A Scoping Review With a Personalisation Lens

Arkadiusz T. Tomczyk (), Christopher Ferraris and Dario Splendido
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Arkadiusz T. Tomczyk: Bournemouth University, Faculty of Business and Law
Christopher Ferraris: Bournemouth University, Faculty of Media, Science and Technology
Dario Splendido: Bournemouth University, Faculty of Media, Science and Technology

Chapter 37 in Information and Communication Technologies in Tourism 2026, 2026, pp 549-565 from Springer

Abstract: Abstract This scoping review maps how artificial intelligence, particularly large language models (LLMs) and adjacent modalities, influence the cultural tourism visitor experience through personalisation. Following Arksey and O’Malley’s PRISMA-ScR reporting guidelines, searches were conducted in Scopus and Google Scholar (2015–2024), yielding 610 records, of which 18 studies were retained after screening. The analysis identifies a layered “personalisation stack” comprising conversational guidance (LLM pilots), knowledge-graph storytelling, behaviour-aware recommenders, telemetry-informed orchestration, and immersive/assistive media. Reported benefits cluster around engagement, perceived relevance, comfort in VR, and progress towards accessibility. However, value is contingent on governance, autonomy calibration, spatial/crowding effects, and explainability. Evidence gaps include longitudinal evaluation, multimodal LLM integration with knowledge graphs and sensor streams, accuracy/bias auditing, privacy-by-design, and performance reporting for real-time operation. The paper synthesises theoretical, managerial, and policy implications and outlines a practice-oriented checklist. Overall, the findings clarify how AI shapes cultural visitor experiences and the safeguards required to deliver equitable value at scale.

Keywords: Artificial intelligence; Cultural tourism; Large language models; Personalisation; Visitor experience; Scoping review introduction (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-23925-9_37

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