Problem
Research questions and friction points this paper is trying to address.
Generative Image AI
Architect-client Communication
Early-stage Design
Shared Understanding
Exploring the Affordances of Generative Image AI for Supporting Early-stage Architect-client Communication
🤖 AI Summary
This study addresses the persistent challenges of inefficient communication and difficulty in establishing shared understanding between architects and clients during early-stage architectural design. Through video-conferenced collaborative experiments incorporating text-to-image generative models, this work investigates how generative artificial intelligence reshapes the communicative dynamics between both parties. The findings reveal that AI-accelerated visualization significantly transforms conversational dynamics, fostering deeper client engagement in shaping design directions and effectively facilitating the construction of a shared understanding. Furthermore, this research highlights that stylistic deviations and the inherent unpredictability of generated outputs remain critical challenges requiring further resolution.
📝 Abstract
Text-to-image generative AI can produce renderings from natural-language prompts in near real time, making it increasingly popular for rapidly visualizing concepts in early-stage architectural design. Meanwhile, exchanging ideas efficiently and building shared understanding have long been central challenges in architect-client communication. How might the speed of generative image AI change this communication? To explore this question, we conducted a study with 11 architect-client pairs, in which each pair used generative image AI over video conference to collaboratively produce early-stage renderings of the client's"dream house."Our findings suggest that generative image AI helped pairs develop a solid shared understanding by providing concrete visual materials and supporting the exchange of ideas. It also shifted conversation dynamics, enabling clients to participate more actively in shaping design direction. However, challenges emerged, including a stylistic bias toward particular types of images and unpredictable shifts in design direction caused by variation across generations. We conclude with implications for the design of future generative image AI-based systems that support architect-client communication.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative Image AI
Text-to-image
Architect-client Communication
Collaborative Design
Shared Understanding
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