Generative AI approaches for architectural design automation

This review examines the potential and challenges of Generative Artificial Intelligence (AI) in automated building design within architectural practice. A comprehensive analysis of advanced generative models is conducted to evaluate their performance across eight architectural criteria. The qualitative assessment indicates that hybrid approaches combining diffusion models with autoregressive techniques provide the most promising outcomes for architectural applications. Despite advancements, significant challenges remain, including scalability limitations, fragmented workflow integration, and the lack of standardized evaluation frameworks. Potential solutions are identified through interdisciplinary collaboration and strategic research directions, such as developing unified evaluation metrics, enhancing model adaptability, integrating energy-optimized design generation for sustainability, and incorporating designer input in AI-driven workflows. This review provides a structured evaluation of current generative design approaches while proposing a roadmap for future research that bridges the gap between AI innovation and practical architectural implementation, ultimately advancing the field toward more efficient, creative, and sustainable building design automation. • Current AI-for-BIM tools act as plugins, perpetuating silos over holistic integration. • Standardized evaluation frameworks needed to address gaps between computational and human metrics. • Generative AI risks repeating CAD's early flaws, favoring task automation over synthesis. • Collaboration is key, moving architects from producers to strategic curators of AI. • Physics-embedded models are essential for co-optimized and truly buildable designs.