This thesis explores the potential of AI generated images as a means to enhance the design, sketching and brainstorming processes in architecture. The study addresses the challenges faced by architects in generating innovative ideas and overcoming cognitive biases during their sketching phase. By examining the integration of the AI inpainting tool, Dall-E 2 developed by OpenAI, into the architectural sketching process, the study explores the possibilities as well as the challenges with such an integration. To do so, a qualitative approach utilizing a case study methodology was employed, conducting a focus group consisting of five architects. The participants were given the task of creating a skyscraper using the inpainting tool individually and to iterate over the sketches in three iterations. Between each iteration, group discussions were held to discuss their experiences and thoughts on the tool itself and the images generated. The data collected from the focus group was transcribed and analyzed using theoretical thematic analysis. The analysis produced four key themes, including human-computer interaction, tool improvement points, evaluation of the inpainting tool, and evaluation of generated images. The results reveal that even though the participants encountered challenges with the inpainting tool’s interaction and output, they still found value in its application to their process.

The findings of this study suggest that AI inpainting effectively can be integrated into the early stages of sketching, providing architects with rapid editing capabilities and alternative design options that align with the characteristics of brainstorming.