This study aims to conceptualize and elucidate an artificial intelligence–based model for shaping the tourist experience and enhancing destination development in the city of Yazd. Positioned as an applied inquiry, the research adopts a qualitative methodology grounded in content analysis. Data were collected during the winter of 2025 through expert interviews and structured questionnaires. The findings reveal that data triangulation—achieved by integrating quantitative indicators with in-depth tourist interviews and multimedia datasets—can facilitate a comprehensive understanding of the tourist experience. Furthermore, in-depth interviews serve as a robust mechanism for validating the results.



The key variables shaping the tourist experience include overall satisfaction, intention to revisit, word-of-mouth recommendation, experiential deepening through participation in events, sustainability and responsible behavior, and accessibility. The study employs a dynamic assessment framework that combines qualitative techniques with artificial intelligence tools, such as machine learning models, deterministic analyses, time-series evaluation, and spatial segmentation.



Additionally, the research underscores the importance of operational mechanisms to ensure transparency, accountability, and privacy protection. These mechanisms encompass data governance, data security and privacy safeguards, model and decision-making transparency, auditing and accountability processes, attention to cultural and social contexts, legal considerations, stakeholder engagement, and transparent dissemination of information.