Generative AI in architecture: Examining text-to-image models and platforms for cultural heritage representation in UAE design

Architectural design shapes cultural identity, particularly in regions like the UAE, where architecture blends heritage and modernity. However, generative AI struggles to accurately capture architectural heritage due to biases in training data and model interpretability. This study evaluates six generative models—Flux, PromeAI, Stable Diffusion XL, DeepAI, Getimg.ai, and ImagineArt—in producing designs across three categories: traditional, modern, and hybrid (modern inspired by traditional). Using CLIP scores to measure textual-image alignment, we found traditional styles were most faithfully generated, while modern designs proved more challenging. Hybrid styles achieved moderate performance, reflecting the complexity of merging cultural motifs. Flux and PromeAI outperformed others, excelling in traditional and hybrid categories. These results underscore the potential and limitations of AI in architectural design, emphasizing the need for refined datasets and training techniques to improve cultural accuracy. By integrating AI tools, architects can enhance conceptual design efficiency, experiment with cultural elements, and streamline ideation. Future research should prioritize culturally diverse datasets, multimodal learning (e.g., 3D generative approaches), and real-time AI-assisted workflows. This study advances AI-driven architectural innovation while promoting culturally authentic design practices.