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

Design thinking concepts such as storytelling, framing, and co-evolution, have been established from close readings of design activity. The increase in easy-to-use computational methodologies provides an opportunity to validate these concepts more widely. Among these concepts, storytelling is already operationalised through various computational approaches. In this paper, we create one corpus of design activity data from the four shared-data DTRS workshops and use Linguistic Inquiry and Word Count (LIWC) in attempting to automatically detect components of stories. However, the conversational nature of the data indicates that further development in methodology is needed. The contribution of the paper lies both in outlining how an automated method for identifying stories could work and showing how the DTRS corpus can be compared with other large datasets outside of the design discipline. This represents a further step on the way to understanding design thinking in conversational contexts.@en

Abstract. Analysing records of design activity such as transcripts or documents have typically involved close reading of transcripts and manual identification of concepts and behaviours. We explore the applicability of a machine-learning based computational tool—called Empath—in identifying high-level patterns in design talk. Specifically, we use it to examine the datasets from the Design Thinking Research Symposium (DTRS) workshops for two contrasting aspects of design talk—the expression of tentativeness that characterises designers’ exploration of the problem-solution space, and the expression of causal reasoning that characterises designers’ analytical thinking. We find that such a tool can be effectively used as a means of “distant reading”. However, the lack of design relevance in the tool’s training data results in ambiguities and mis-categorisations that still need resolution through close reading. @en

A necessary condition of understanding how designers work is understanding how designers talk. In this paper we show how new methods of linguistic data analysis are beginning to reveal insights into the general nature of design conversations. For the first time we combine design activity data collected over 30 years by the Design Thinking Research Symposium (DTRS) ‘shared data’ series into a single corpus. We apply emerging techniques of analysis on this corpus and explore word forms, expressions, topics, and themes related to the particularities of how designers talk. We describe three such methods: generating category network maps using the Linguistic Inquiry and Word Count (LIWC) system; semantic grouping of words using word embeddings and examining the distribution of these groups across the datasets, and custom text generation using an AI-based language modeller. In applying these methods, we show that exploring design activity data at the corpus level can reveal more general patterns of design talk and raise key questions and hypotheses for further study. We see these methods as a first step in developing an understanding of how people not considered to be designers (e.g., scientists, business people, politicians) talk in ways that might be considered ‘designerly’.

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Board games are fertile grounds for the display of social signals, and they provide insights into psychological indicators in multi-person interactions. In this work, we introduce a new dataset collected from four-player board game sessions, recorded via multiple cameras, and containing over 46 hours of visual material. The new MUMBAI dataset is extensively annotated with emotional moments for all game sessions. Additional data comes from personality and game experience questionnaires. Our four-person setup allows the investigation of non-verbal interactions beyond dyadic settings. We present three benchmarks for expression detection and emotion classification and discuss potential research questions for the analysis of social interactions and group dynamics during board games.

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