A User Experience–Based Evaluation Model for AI-Enabled Digital Booking Engines in Tourism

Digital booking engines play a central role in contemporary tourism services, increasingly integrating artificial intelligence to personalize interactions and optimize decision-making processes. While these systems are widely adopted, there is a lack of comprehensive evaluation frameworks that integrate user experience (UX), service design principles, and emerging ethical considerations related to artificial intelligence (AI) enabled services. This paper proposes a UX-based evaluation model for AI-enabled digital booking engines in tourism, grounded in a multidisciplinary perspective that combines service design, human-centered design, UX evaluation models, and ethical guidelines for trustworthy AI. The model is operationalized through a structured evaluation questionnaire covering seven dimensions: tourism, usability, navigability, aesthetics, intelligence, ethics, and sociability. Methodologically, the study employs a qualitative, exploratory approach. An initial version of the model is applied illustratively as a proof of concept using a small, heterogeneous sample of users interacting with a widely used tourism booking platform. The exploratory application aims to assess the coherence, applicability, and refinement needs of the proposed model rather than to provide statistically generalizable results. The main contribution of this research lies in the conceptual and methodological articulation of an integrative evaluation framework that supports the assessment and improvement of AI-enabled tourism booking services from a user-centered and ethically informed perspective. Limitations related to sample size and empirical validation are acknowledged, and directions for future large-scale validation studies are outlined.Acknowledgements: The authors would like to thank the individuals who participated in the exploratory application and expert review of the evaluation model. Their time, feedback, and constructive insights were invaluable in identifying areas for refinement and supporting the iterative development of the proposed evaluation framework. All contributions were provided voluntarily and in accordance with applicable data protection regulations.DOI: https://doi.org/10.54663/2182-9306.2026.SpecialIssueCMTH.230-252