Is a Picture Worth a Thousand Words? Comparative Evaluation of Generative AI for Drawing and Representation

The paper presents a comparative evaluation of generative AI systems for drawing and architectural representation, focusing on the operational principles, workflows, and visual outputs of text-to-image applications such as Midjourney, Stable Diffusion, and DALL-E 2. The research analyzes neural networks, latent space mechanisms, generative adversarial networks, autoregressive models, and diffusion probabilistic models to explain how AI systems transform textual prompts into visual representations. Through experimental prompt engineering and comparative image generation tests, the study investigates the relationship between AI-assisted creativity, visual storytelling, representation processes, and design workflows. The paper critically discusses the implications of generative AI for architecture and visual culture, including authorship, ethics, copyright, bias, realism, and the transformation of creative practices. The research ultimately proposes an informed and critical approach to AI-assisted representation, emphasizing the evolving role of designers as curators and strategic decision-makers within AI-driven creative environments.