Article

Objectivation in design team conversation

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Abstract

In this article we report our study of objectivation in the conversation of a design team. Objectivation is the practical work in which groups engage to produce social objects that facilitate orderly collaboration. We observed how design team members came to agree on specific details about an educational simulation they were designing, as they treated simulation features like independent social facts that could be affected by and have effects on other simulation features, and that had discrete benefits that made them an asset within the product. In our report we describe patterns of objectivation in their conversation that produced these results. We conclude by discussing how our study relates to, and enriches, the findings provided by prior design research.

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... The design methods literature is largely focused on demonstrating the efficacy of creative and generative techniques that focus more on measuring outcomes than looking at the quality of the process itself as Toh & Miller [1] note. Yet actual practices of design hold great complexity in the interaction between people, ideas, and environments, particularly in the unfolding and co-evolution of problems and solutions [2], and recent studies have focused more directly on design conversation in order to understand the linguistic mechanisms through which language creates and 'configures' the social objects of a design process [3][4][5][6][7]. ...
Conference Paper
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.
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