Generative AI in Architectural Conceptual Design: A Structured Scoping Review of LLMs, Text-to-Image Diffusion Models, Spatial Layout Generation, BIM-AI Coupling, and Human-AI Workflows
Abstract
This article reports a structured scoping-style review, rather than a meta-analysis, of generative artificial intelligence (GenAI) applications in architectural conceptual design. Searches were conducted in Web of Science, Scopus, Dimensions and CNKI for English- and Chinese-language records, supplemented by targeted forward and backward citation chasing. The evidence base distinguishes coded studies from contextual references and comprises 58 coded studies and 15 contextual references published between 2018 and 2026. The literature was coded by technical type, design stage, input modality, output modality, evaluation strategy, editability, and stated limitation. The synthesis identifies four interrelated domains: LLM-based semantic and knowledge support, diffusion-based conceptual visualization, spatially conditioned layout and 3D scene generation, and BIM/parametric coupling within human-AI workflows. The review indicates that current research is strongest in atmospheric visualization and prompt-mediated exploration, while evidence for architectural validity, downstream editability, regulatory checking, and professional accountability remains limited. Four cross-cutting challenges—controllability, evaluability, translatability, and responsibility—are operationalized as review-derived evaluation dimensions. GenAI is therefore better understood as a representational and workflow technology for early-stage exploration than as an autonomous architectural designer.
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