Personas are hypothetical representations of real-world people used as storytelling tools to help designers identify the goals, constraints, and scenarios of particular user groups. A well-constructed persona can provide enough detail to trigger recognition and empathy while leaving room for varying interpretations of users. While a traditional persona is a static representation of a potential user group, a chatbot representation of a persona is dynamic, in that it allows designers to “converse with” the representation. Such representations are further augmented by the use of large language models (LLMs), displaying more human-like characteristics such as emotions, priorities, and values. In this paper, we introduce the term “Synthetic User” to describe such representations of personas that are informed by traditional data and augmented by synthetic data. We study the effect of one example of such a Synthetic User – embodied as a chatbot – on the designers’ process, outcome, and their perception of the persona using a between-subjects study comparing it to a traditional persona summary. While designers showed comparable diversity in the ideas that emerged from both conditions, we find in the Synthetic User condition a greater variation in how designers perceive the persona’s attributes. We also find that the Synthetic User allows novel interactions such as seeking feedback and testing assumptions. We make suggestions for balancing consistency and variation in Synthetic User performance and propose guidelines for future development.

Reflection plays a vital role in the development of designers, enabling them to evaluate their experiences, enhance their learning, and foster professional growth. This research analyzed reflections of 56 design students, as part of graded coursework, using content and dictionary-based approaches (LIWC). Building on an existing model of reflection with eight components (experience, belief, difficulty, perspective, feeling, learning, intention, and descriptive) we identify, using descriptive statistics, the linguistic features associated with each component and correlate these to grades achieved. We distinguish two types of reflections associated with higher grades: those emphasizing personal experiences that we term holistic narrators, and those that focus on critical self-evaluation that we term in-depth explorers. Our results provide insights for design educators, guiding interventions to enhance critical thinking and self-reflection among design students. They also inform the development of automated tools to assess and enhance reflective practice in educational and design settings.

Organizational transitions can evoke a range of reactions and emotions among employees, departments, stakeholders, and leaders. To effectively manage the transition, it is crucial to comprehend how organizations experience and design change to navigate the various challenges of the transition process. This study investigates how changes in the working environment of the catering staff of a convention centre in the Netherlands led to the formation of a close-knit community that not only embraced the change but also developed innovative approaches for addressing local and social challenges. We draw on interviews with 16 individuals from the organization and use social practice theory to show the interconnected elements of practices that collectively constitute a creative approach to change. The study reveals that leadership and support are crucial competencies for promoting the participation and engagement needed to turn a crisis into an opportunity for both the organization and its stakeholders.

Design practices are being increasingly adopted by governments worldwide. Yet, barriers to design practices have been noted. Among the various barriers identified, a recurring theme is the gap between design practices and the established work practices of governments, suggesting that changes are needed on both sides—government organizations and design practices. In this paper, we present a study about how design practices become stabilized in the long term within local government organizations, drawing on organizational theory. The findings reveal that different types of legitimacy for design practices—pragmatic, moral, and cognitive—were shaped over time in different organizations, closely tied to each organization’s context and needs. Moreover, how design practices were interpreted and legitimized within an organization influenced what organizational processes and structures were developed to support them. This study demonstrates that the stabilization of design practices within government organizations is an adaptative process between the organization and design practices. We argue that this process is facilitated by the continuous efforts of design stakeholders in the organization.

This Conversation at DRS2024 in Boston, attracting around 30 participants, centered on the evolving role of design within multidisciplinary AI teams, particularly in the context of the development of clinical AI applications. As AI is entering healthcare, questions are being raised if and how designers can contribute to AI-driven clinical solutions and whether they need to develop potential other skills and responsibilities. Drawing from ongoing research and insights from various workshops and interviews, the conversation highlighted the importance of the negotiation of agency between humans and machines in clinical settings, translational design for patients, data hierarchy and its impact on design, and finally the importance of language and storytelling in framing interactions mediated by AI.

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 chapter, we create one corpus of design activity data from the four shared-data Design Thinking Research Symposium (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 chapter 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.

Educational systems face increasingly complex demands, confronting teachers with multidimensional people-centred problems rarely solved by linear or standardised solutions. Nevertheless, teachers must juggle multiple variables simultaneously in their daily work. This can lead to routine and unreflective decisions that do not consider unique situations. Considering that designers’ abductive reasoning could support problem-framing skills, this article discusses how a design thinking approach can contribute to developing reflective teaching practice. This case study explores how 20 Chilean teachers define, frame, and re-frame their pedagogical problems in a design-based teacher professional development programme. Findings revealed three problem-framing triggers that support teachers’ reflection: (a) collaborative discussions, (b) awareness of people and their context, and (c) visualising, making, and testing ideas. Combined, they articulate action and promote reflection, demonstrating the value of a design thinking approach in supporting teachers’ pedagogical decisions.

Analysing transcripts of design activity typically involve either close reading or manual coding of data, which limits the amount of data that can be analysed. In contrast, we explore a machine-learning based linguistic analysis tool called Empath to identify patterns of reasoning in design talk. The data we use derives from the Design Thinking Research Symposium (DTRS) shared-data workshops which we analyse to look at two contrasting aspects of design talk: the expression of tentativeness, characterising designers' generative thinking; and the articulation of explanations, characterising their deductive or analytical thinking. We show, at the level of speech turns, how tentativeness and explanation relate to, and overlap, each other. Finally, we discuss the limitations of this ‘linguistic analysis at scale’ approach.

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.

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.

In the midst of climate change, and the need to seek more sustainable ways of living, design is increasingly tackling problems at a societal level. This paper reflects on a strategic design project at a Brazilian foundation focused on sustainable development in the Amazon rainforest region. In this study, we asked what contributions design can bring to organisations involved in addressing development issues. The paper describes several experiments and strategies to make it tangible to non-designers howa design-led process unfolds and how design can support the organisation's efforts in delivering value to the communities they serve. The case study offers an example on how design practices combined with systemic approaches can spark increasing levelsof collaboration across siloed departments

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.

Knowledge on how design practices evolve and become part of the daily practices of public organisations is still lacking. Prior to embarking on this research, we asked ourselves how this phenomenon should be studied in the context of public organisations. In the complex system of an organisation, practices are in constant flux, making it difficult to understand how a practice evolves based on certain factors such as leadership, legitimacy, or organisational culture. Adopting an alternative approach, including ‘time’ as an analytic element, we attempted to understand the evolution of design practices through a sequence of relevant past events. In a series of case studies of local government, we collected data through publicly available organisational documents related to design practices and visualised these data in a timeline. With the results, we constructed a narrative on how design practices have evolved over time in these organisations. This paper describes one case study at Kent County Council in the UK showing how this document-based, process-oriented research approach allowed us to capture the evolution of design practices in a public organisation over a 12-year period within a short research time and with greater objectivity. In conclusion, we argue that this longitudinal research method can be a new approach for researchers conducting studies on design practices within public organisations.

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.

In recent years, governments have increasingly pursued innovation by embed-ding design into their organizations. One particularly common approach to em-bedding design in government organization is to establish public sector innova-tion labs. These labs are described as contributors and facilitators of innovation in policymaking processes; however, less light has been shed on the role of in-house designers (including these labs) in fostering and managing the changes made by design practices within government organizations. In the current study, design management has been used as a theoretical lens to study the strategic activities of in-house designers in a Dutch municipality to embed design within the organization. The findings show the importance of strategic activity by in-house designers to foster design practice and resulting organizational changes and the need for participation of more organizational members in this activity. We conclude with setting an agenda for more research and practices on strategic activities to foster design practices and organizational changes in government.

From diagnosis to patient scheduling, AI is increasingly being considered across different clinical applications. Despite increasingly powerful clinical AI, uptake into actual clinical workflows remains limited. One of the major challenges is developing appropriate trust with clinicians. In this paper, we investigate trust in clinical AI in a wider perspective beyond user interactions with the AI. We offer several points in the clinical AI development, usage, and monitoring process that can have a significant impact on trust. We argue that the calibration of trust in AI should go beyond explainable AI and focus on the entire process of clinical AI deployment. We illustrate our argument with case studies from practitioners implementing clinical AI in practice to show how trust can be affected by different stages in the deployment cycle.

The formulation of questions in processes of design is an activity affected by cognitive biases inherent to humans. Cognitive biases, developed through gaining experience, influence how decisions are made during problem solving. When an outcome is predictable, experience provides mental shortcuts or heuristics to enable the problem solver to act effectively. When an outcome is uncertain, cognitive biases can wrongfully project preconceptions, elevate self-interest, and undermine the problem solver’s greater ambitions for positive impact. Mitigating cognitive bias is thus vital for design problem solving under conditions of uncertainty. Designers explore uncertainty through an approach typified by human empathy, problem framing, and creativity. This chapter reveals the nature of asking effective questions within designerly thinking. This means understanding nuances of context, surfacing novel insights about how a system performs, and crucially working out how people within systems experience the world around them.

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’.