Shaping Architecture with Generative Artificial Intelligence: Deep Learning Models in Architectural Design Workflow

Deep-learning generative AI promises to transform architectural design, yet its potential employment and ready-to-use capacity for everyday workflows are unclear. This study systematically reviews peer-reviewed work from 2015–2025 to assess how GenAI methods align with architectural practice. Following database searches and subject-area filtering, 42 studies were included from 1,566 records. Each was evaluated with a five-indicator, three-tier rubric: Output Representation Type (ORT), Pipeline Integration (PI), Workflow Standardization (WS), Tool Readiness (TR), and Technical Skillset (TS). Results show outputs are concentrated in non-native formats (≈40% raster imagery; ≈45% meshes/voxels/graphs), with relatively few CAD/BIM-native results (≈15%). Toolchains are often fragmented (PI: ≈43% Tier-0 with ≥4 steps; ≈40% Tier-1 with 2–3 tools; ≈17% Tier-2 single-platform). Most studies map to schematic-only design stage (WS Tier-1 ≈69%), and multi-stage, CAD/BIM-compatible pipelines remain uncommon (WS Tier-2 ≈12%). Prototypes frequently require bespoke coding (TR Tier-0 ≈65%) and advanced expertise (TS Tier-0 ≈74%). These findings indicate a persistent gap between experimentation with ideation-oriented GenAI and the pragmatism of CAD/BIM-centered delivery. Advancing practice readiness will require native CAD/BIM outputs, tighter plug-in/API integration, tools that bridge heterogeneous file formats and metadata export, and packaging ML modules into CAD/BIM environments that lowers skill demands. Limitations include the academic focus of the corpus and rapid field evolution.