Theory of Architecture as the Maestro Organising AI Text-to-Image Prompts

In the wake of the Fifth Industrial Revolution, artificial intelligence (AI) has become a disruptive force in architectural design processes. One of its techniques is text-to-image, which generates visual representations from textual descriptions. This research questions how architects and students organise text-to-image prompts. Unfortunately, AI images have neglected the basic principles of architectural theories. The problem explored here is whether AI-generated images truly reflect architectural theory or replicate styles without deep understanding. This research aims to propose a chart of semantic textual models that employs selective keywords, inspired by the characteristics, ideas, and conceptual statements associated with theories of architecture, to organise text-to-image prompts. The study followed scientific methodology, began with a literature review, and then analysed previous readings that highlighted this gap and proposed solutions. It concluded by determining the components (variables) of the prompt structure. Using three AI platforms, the researcher conducted visual experiments, injecting five theories into the prompts to compare images before and after. The analysis of these images was transparently validated through a rubric-based evaluation distributed to independent evaluators. As a result, the (after) images were improved, expressing the theories’ characteristics and conveying symbolic meanings. The conclusion is that AI architectural images must have a maestro to organise prompts. This maestro is the ‘Theory of Architecture’, which is expected to bridge the gap between AI’s imagination and authentic design principles.