Artificial Intelligence for Sustainable Architectural Design: Global Trends, Transparency Gaps, and Future Roadmaps
In response to accelerating global challenges such as resource depletion, climate risk, and urban health inequality, architectural design is undergoing a fundamental shift from experience-based approaches to intelligence-driven practices. Artificial Intelligence for Sustainable Architectural Design (AI4SAD) has emerged as a critical catalyst in this transformation. This study provides the first comprehensive mapping of AI4SAD research, examining how AI is being applied across architectural design stages, sustainability objectives, and algorithmic domains.
Scope and Methodology of the Systematic Review
The research is grounded in a systematic review of 408 scholarly studies, offering a robust spatiotemporal overview of global AI4SAD development. By analyzing patterns across regions, time periods, design phases, and sustainability targets, the study establishes a structured evidence base that reveals dominant research trajectories as well as underexplored areas within AI-driven sustainable design.
AI Applications Across Design Stages and Sustainability Goals
Findings indicate that AI4SAD applications span multiple stages of the architectural design process, from early conceptual generation to performance evaluation and optimization. These applications align with a range of sustainability goals, including energy efficiency, environmental impact reduction, and occupant health. The study highlights how different AI algorithms are preferentially deployed at specific design stages, shaping both design workflows and outcomes.
Transparency Deficit and Its Implications
A central contribution of the study is the identification of a significant transparency deficit in current AI4SAD research. Only a limited number of studies disclose essential details such as data sources, model parameters, or measurable performance gains. This lack of transparency undermines reproducibility, limits cross-scenario transferability, and hampers the accumulation of shared knowledge, posing a critical challenge to the field’s long-term advancement.
Three-Stage Development Framework for AI4SAD
To address these challenges, the paper proposes a three-stage development framework for AI4SAD. The analysis reveals an ongoing transition from level 2 systems, characterized by specialized and task-specific models, toward level 3 foundation models with broader generalization capabilities. This framework provides a structured lens for understanding the evolution of AI applications in sustainable architectural design.
Roadmap for Responsible and Scalable AI Integration
Building on the review findings, the study establishes a forward-looking roadmap that prioritizes improvements in generalizability, autonomy, and interpretability. By emphasizing responsible AI practices and transparent reporting, the roadmap aims to guide future research and practice toward more reliable, transferable, and ethically grounded AI-driven design solutions that support sustainability goals in the built environment.
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