Urban Digital Twins (UDTs) are dynamic digital representations of cities, integrating heterogeneous data sources and advanced modelling techniques to simulate urban processes and support decision-making. While UDTs are increasingly adopted by municipalities, their use remains largely expert-driven, limiting their potential as participatory infrastructures.
A critical challenge concerns how to actively engage citizens in urban decision processes through Digital Twins in ways that are informed, responsible, and cognitively meaningful. Participation cannot be reduced to mere access to information: it requires mechanisms that foster understanding, sustained motivation, and progressive skill development across heterogeneous populations with different levels of expertise.
The candidate will be requested to investigate cognitive and motivational mechanisms that enable effective citizen engagement through UDTs. The research will explore advanced methodologies — including gamification strategies, adaptive interfaces, explainable AI techniques, and personalized interaction models — to support incremental learning, long-term engagement, and responsible participation in complex urban decision scenarios.
The expected contribution is twofold: (1) advancing theoretical understanding of how digital simulation environments influence cognition, motivation, and civic decision-making; (2) providing evidence-based design principles for participatory UDTs capable of fostering informed and active civic engagement.
