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TRIGGERED: Using human-AI dialogue for problem understanding in collaborative design

Creative conversations among designers and stakeholders in a design project enable 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 revealed the exclusionary spaces 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.

“Anne’s project breaks exciting new ground by using AI dialogue to help designers work on real-world complex social problems.”
Focus group participant

This project explores how a text generator based on a language model could synthesize and narrate opinions and experiences that may be unfamiliar to designers. By analysing the conversational exchanges between designers and the designer and the AI, observations are made on how the use of AI leads to prompting nuanced interpretations of problems and ideas, opening up the objective problem and design lenses and interpretations.

We show how AI suggestions can aid divergent thinking by helping designers to think of new problem dimensions and explore the unforeseen consequences of proposed solutions. This can mitigate designers making presumptions about what the design problem is, and allow designers to consider more nuanced aspects of the problem. In this sense, a conversation with AI can be seen as getting beyond the rationality of the designers, taking them to areas of the problem that they would not intuitively go to given their preconceptions and natural biases.