The integration of Artificial Intelligence (AI) into urban planning holds transformative potential for understanding and shaping urban behaviors, yet empirical research linking AI systems to behavioral outcomes remains scarce. This study investigated how AI can be leveraged to decode urban dynamics, employing a qualitative grounded theory design. Data were collected through systematic document analysis of ten smart city policy reports and technical assessments from Singapore, Barcelona, and New York City, complemented by secondary case evidence drawn from published municipal reports and academic studies on London, Tokyo, Copenhagen, and Los Angeles. Findings revealed five core dimensions of AI’s impact: real-time mobility optimization, behavioral nudges for sustainability, predictive resource allocation, ethical and equity challenges, and shifts in urban governance. The study contributes a grounded theoretical model of AI-mediated behavioral modulation, demonstrating that while AI can foster efficient and sustainable behaviors, its benefits are contingent upon transparent, inclusive design. The paper concludes with policy recommendations, acknowledged limitations, and a future research agenda.
Dorostkar et al. (2026) studied this question.