AI Solutions for Urban Innovation  

APPLIED AI IN AEC RESEARCH STUDIO

This research studio trains AEC students to design, prototype and pilot AI-driven products for city-scale problems. Combining product-led workflows (user research → MVP → metrics → iterative growth) with rigorous research methods, the course guides teams from problem formation through data pipelines, model design, UX, deployment and ecosystem integration. Projects target urban domains: mobility, housing, energy, climate resilience, infrastructure, and inclusion while prioritizing pragmatic, non-disruptive interventions that improve existing workflows.

The studio emphasizes concrete technical and social competencies: urban data collection and curation, pipeline engineering, model selection and validation, interface design for diverse stakeholders, and strategies for pilots, procurement and scaling. 

Key data challenges in modern cities that projects will confront include: fragmented and siloed data sources; poor data quality and inconsistent formats; limited spatial or temporal resolution; sensor heterogeneity and maintenance gaps; scarcity of labeled ground truth; privacy, consent and governance constraints; algorithmic bias and representational inequity; interoperability with legacy systems and procurement barriers; and the need for real-time processing/scalable architectures under resource constraints. Addressing these challenges is central to creating responsible, deployable urban-tech solutions. Evaluation combines participation, documented research deliverables, and a final product judged for technical rigor, design quality, societal impact and feasibility.

Learning Objectives
The studio equips students with essential theoretical foundations and practical capabilities for developing AI-driven methodologies addressing architectural and urban challenges. Specific learning objectives include:

• Mastering AI principles and applications specific to the built environment
• Developing robust data collection, transformation and management strategies
• Designing and implementing appropriate AI methodologies for architectural problems
• Creating intuitive interfaces for AI model interaction and deployment
• Crafting compelling visualizations of complex spatial data relationships
• Developing comprehensive project management approaches for AI-integrated architectural processes