A Decision Support System Integrating Extended Reality and Conversational AI for Participatory Urban Planning

Urban planning increasingly depends on methods capable of capturing citizen perspectives in forms that are both inclusive and analytically useful for decision-making. Conventional participation mechanisms, such as public meetings, paper questionnaires, and online platforms, often suffer from low reach, strong self-selection effects, and weak suitability for structured comparative analysis. This paper presents XRCity, a decision support system that combines extended reality, conversational artificial intelligence, and a planner-side backend to support participatory urban planning in public spaces. The system is centered on Olivia, a life-sized virtual assistant deployed on outdoor interactive screens, and on a backend environment that enables planners to prepare knowledge resources, configure interaction scripts, validate conversational behavior, process transcripts, and analyze elicited opinions. The contribution of the paper is not just the presentation of an XR interface, but the description and validation of a complete decision-support pipeline that connects campaign design, citizen interaction, opinion structuring, and planner-side analytics. The system was validated through real-world deployment in Torres Vedras, Portugal. Across more than 250 interactions and over 740 min of conversation, 191 usable sessions were analyzed, showing an average of 6.7 messages per user and 2.8 min per interaction. Of these sessions, 14.7% produced at least one structured response to an urban planning question, exceeding the project target of 10%. These results indicate the operational feasibility of using public-space conversational XR to elicit analyzable planning input, while a formal validation of the opinion-matching step remains future work.