There are substantial opportunities in the construction industry to reduce energy costs, decrease global material use, and decarbonize our society. On opportunity lies in the design stages of a building. Key decisions affecting a building’s long-term environmental impact, such as material selection and design for disassembly, are often made at a late stage when there is limited flexibility to make changes. If these decisions were made earlier, it could help mitigate unnecessary waste, resource depletion, and environmental impacts. This project will address this challenge by leveraging artificial intelligence (AI) and life cycle assessment. This research will provide practical, science-based tools to guide early design decisions, directly contributing to a more resource-efficient construction industry. This project will benefit society by advancing education and workforce development, integrating its findings into university curricula, mentoring students, and engaging industry stakeholders in circular building practices. This project will develop a data-driven framework that integrates generative AI (GenAI), life cycle assessment, and circular economy principles to optimize material selection and waste reduction in the built environment. A key innovation of this work is an AI-driven system that generates technical specifications for materials and assemblies based on early-stage design parameters. These specifications will be evaluated using quantitative circularity indicato