Intelligent Conversational Agents for Sustainable Tourism Planning: Architecture, Implementation, and Technical Evaluation of an AI-Driven Itinerary Generation System

The tourism industry faces increasing demand for personalized travel services alongside environmental sustainability requirements. Recent developments in generative artificial intelligence and large language models provide mechanisms for assisting travelers with itinerary planning, although their integration with tourism data services and sustainability criteria remains under investigation. This paper presents the design and technical evaluation of a conversational agent for sustainability-aware tourism planning in a Technology Readiness Level (TRL) 4 experimental environment. The system combines large language model-based interaction with an external flight information service to generate structured itineraries covering transportation, accommodation, and activities. Sustainability considerations include externally supplied flight emissions information and qualitative recommendation rules for other itinerary components. The controlled evaluation examines functional correctness, natural language processing, external service coordination, response time, and the inclusion of sustainability information. The results indicate that the components can be integrated under the evaluated conditions, while also identifying limitations related to heterogeneous data sources, environmental impact estimation, and the absence of real-world user deployment. The findings concern technical feasibility and do not demonstrate behavioral change or reductions in trip-related emissions.