AI is positioned as a practical tool in Local Digital Twins (LDTs) to enhance urban resilience by improving decision-making rather than offering instant solutions. The challenge lies in leveraging AI effectively to process data, identify patterns, and support better planning, resource allocation, and city responsiveness, while managing expectations and hype. The solution involves applying AI within the LDT framework as both rule-based and learning-based systems. These systems aid decision-making by either executing predefi ned rules or uncovering patterns in data. The key is to choose the approach based on context and available resources. Lightweight algorithms, which are simple yet effective, offer substantial value while avoiding the pitfalls of overcomplicated AI technologies. The learning unit conveys the foundational understanding of AI’s role in LDTs. It employs the Triple Loop Learning framework to teach participants to grasp AI fundamentals, apply them contextually, and critically assess their effi cacy. This curriculum is structured to build confi dence, encourage pragmatic thinking, and prepare learners for advanced AI applications in future modules.