The study focuses on the development and validation of a generative AI model to select attractions and construct structured travel itineraries. Using three-stage systematic framework, the model synthesizes information on user preferences, attraction characteristics, time-of-day considerations, and contextual factors to generate coherent daily travel plans. Multiple real-world destination cases were used to evaluate the consistency, personalization quality, and logical structure of the generated itineraries. Preliminary findings indicate that generative AI can integrate diverse qualitative inputs and produce customized, context-aware itineraries that go beyond rule-based recommendation tools. The results demonstrate the feasibility of using generative AI as a decision-support layer in tourism planning.
