Triggered

Abstract

Creative conversation among designers and stakeholders in a design project enables new ideas to naturally originate and evolve. Language allows for the exchange of values, priorities, and past experience whilst keeping solution forms usefully ambiguous. Yet there is a danger that only the language of people directly involved in the design process gets to be heard, limiting how inclusively the problems are interpreted, which in turn can impede how complex design problems are addressed. Recent advances in artificial intelligence (AI) have shown the exclusionary spaces that are often inhabited by designers, engineers, and developers of new artefacts and technologies. On the other hand, text data used to train language models for machine learning applications have the potential to highlight societal biases in ways that designers can utilise. In this paper, we report the results of an exploratory study using AI text generation to synthesize and narrate opinions and experiences that may be unfamiliar to designers. Three pairs of designers were given a complex socio-technical problem to solve. Of these, two pairs interacted with an AI text generator during the task, while one pair acted as a baseline condition. Analysing the conversational exchanges between the designers and the designers & AI, we observe how the use of AI leads to prompting nuanced interpretations of problems and ideas, opening up the objective problem and design lenses and interpretations. Finally, we discuss how the designers (re)assign different roles to the AI to suit their creative purposes.