Boston, a city renowned for its academic traditions and historical significance, is our setting for DRS2024. And Northeastern University’s College of Arts, Media, and Design is our host institution, in partnership with the Harvard University Graduate School of Design and the Massachusetts Institute of Technology Morningside Academy for Design. The call for papers for DRS2024 resulted in a huge response, with 1184 abstracts and 869 full paper submissions—over a 47% increase from DRS2022. Of these submissions, 78 were desk rejected and 791 papers underwent rigorous peer review. Based on this review process, we are pleased to include 386 accepted papers in our program, reflecting a 44.4% acceptance rate. This rigorous selection process ensures that we continue to uphold the highest standards of academic excellence in design research.@en

Design thinking concepts such as framing, storytelling, and co-evolution, have been widely identified as part of design activity though generally have been evidenced from manual coding of design conversations and close reading of transcripts. The increase in easy-to-use computational linguistic methodologies provides an opportunity not only to validate these concepts, but compare them to other kinds of activity in large datasets. However, the process of systematically identifying such concepts in design conversation is not straightforward. In this paper we explore methods of linguistic analysis for revealing problem frames within design process transcripts. We find that frames can be identified through n-grams with high mutual information scores, used at low frequencies, along with subsequent lexical entrainment. Furthermore, we show how frames are organised in primary and secondary structures. Our results represent a step forward in computationally determining frames in datasets featuring design, or design-like activity.@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