This workshop explores the transformative potential of generative artificial intelligence (GenAI) in design research. GenAI, capable of creating new content such as images, text, music, video, and code, raises important questions about authorship, agency, and design practice. Inspired by Roland Barthes’ "The Death of the Author," this workshop examines how GenAI reshapes design research roles and methods. Key topics include best practices, ethical considerations, knowledge generation, and collaboration patterns between human and AI creatives.

Building on themes identified in the successful DIS 2023 workshop, this 2-day event invites designers and researchers to present completed projects, works-in-progress, and theoretical provocations. The structure allows time for both presentations and in-depth discussions, aiming to develop an online resource library and a collaborative publication. The workshop seeks to advance the discourse on GenAI, addressing its challenges and opportunities in design research.@en

This one day workshop will explore the use of Generative Artificial Intelligence (GenAI) in design research and practice. Generative technologies are developing rapidly and many designers are using them. Yet, there remains little published work on the use of GenAI in design. Our goal is to not only showcase the potential of GenAI for design, but to engage in discussions of its shortcomings and opportunities as they have been already articulated by scholars. By synthesizing both published and unpublished works, we will develop best practices, ethical considerations, and future research directions for the use of GenAI in design. We will explore a range of topics and themes, including leveraging the characteristics of GenAI for design, mapping the diverse applications of GenAI in design, envisioning a framework for design, and guiding future work on GenAI in design research. Ultimately, we hope to provide a roadmap for the integration of GenAI into the design research process and to encourage designers and researchers to explore the potential of GenAI in a thoughtful and deliberate way.

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Artificial intelligence (AI) is increasingly being viewed as a creative partner rather than as a tool. How to design such collaborations is still a subject of speculation. In this pictorial, we propose a collaborative role for AI to prompt self-reflection. We explore this through a practice-based inquiry of whether and how AI could help a designer reflect on and relate to their own work. Three designers annotate a collection of images representing their fascinations, with subjective labels, indicating different dimensions of their visual concepts. These labels are used to teach an object detection model the designers’ perspectives. Then, they used this trained model on their own design work to evaluate the AI's potential to prompt self-reflection. By describing this process of AI-training we explore how an AI can help us become aware of our own implicit perspectives.@en

Emerging practices of using ‘off the shelf’ AI as a creative partner in design processes are receiving increasing attention in design research. This paper takes the well-known concept of ‘framing’ in design, along with the Schönian concept of ‘surprise’ to explore how a human-AI dialogue could work. The approach taken is practice-based, with the human designer documenting her process of inquiry and decision making. We show how artificial creativity is expressed through misfiring object detection algorithms, and further how these ‘mistakes’ can be perceived and interpreted by the human designer. The contribution of the research is in laying the foundations for a novel human-AI dialogic practice.@en

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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