A Generative AI Framework for Adaptive Residential Layout Design Responding to Family Lifecycle Changes

Rapidly evolving family structures have intensified the demand for residential layouts that can flexibly adapt to diverse spatial and functional needs. Conventional design approaches, whether manual or computer-aided, often fail to maintain user-centered adaptability across the household lifecycle. Meanwhile, advances in generative artificial intelligence have introduced new opportunities for intelligent design generation; however, existing models tend to prioritize visual aesthetics over behavioral and functional coherence. This study proposes an integrated text-to-design workflow that transforms user requirements, extracted from different family lifecycle stages, into structured prompts for AI-driven spatial generation. A dedicated interior dataset is constructed to incorporate lifecycle responsiveness, user preferences, and spatial functionality, while a composite loss function is introduced to enhance diffusion model precision and contextual fidelity. Comparative experiments against mainstream models such as Stable Diffusion and MidJourney reveal superior adaptability, spatial rationality, and user alignment. Overall, the findings demonstrate the potential of generative AI to bridge user behavior analysis with architectural logic, promoting data-driven, adaptive, and human-oriented residential design practices.