In recent decades, design creativity and design theory have made great progress in terms of understanding and supporting the logic of engineering design for breakthrough and disruptive innovation. Design for transition relies on these new methods, but it also requires the capacity to be creative to facilitate more effective preservation – whether in terms of natural resources, biodiversity, energy, ways of life or other factors. Design for transition calls for a type of engineering design that is not Schumpeterian, not a ‘creative destruction’, but rather a design that manages creative preservation, creativity for better preservation and preservation for improved creativity. In the first section, we clarify the notion of creative preservation for transition; in the second section, we show how creative preservation can be addressed by recent advances in design theory, namely, C-K/Topos. Finally, in the conclusion, we demonstrate the implications of C-K/Topos for the management of the unknowns of transitions and the underlying logic of creative preservation.

How can we theorize about democracy? We can identify the major topics that form the focus of democratic theorists (and others traversing the field), such as democracy's meaning and value. This article focuses on the methodological lenses through which the topics have been and can be viewed. Different lenses bring into focus different phenomena, questions, and problems of democracy. It is argued that the lenses that bring conventional democratic theory approaches into view can provide an unnecessarily narrow and restrictive perspective. Donning different methodological lenses can introduce alternative perspectives, such as renewed attention to value pluralism and the “everyday.” The article sketches four “circles” that capture different potential types of and sources for theoretical work, some of them radically unconventional. It concludes by discussing the specific example of how methods and assumptions of design theory can prompt promising new approaches to theorizing about democracy.

Large Language Models (LLMs) have advanced the extraction and generation of engineering design (ED) knowledge from textual data. However, assessing their accuracy in ED tasks remains challenging due to the lack of benchmark datasets specifically designed for ED applications. To address this, the study examines how theoretical concepts from Axiomatic Design Theory—such as Functional Requirements, Design Parameters, and their relationship—are expressed in natural language and develops a systematic approach for annotating ED concepts in text. It introduces a novel dataset of 6,000 patent sentences, annotated by domain experts. Annotation performance is assessed using inter-annotator agreement metrics, providing insights into the challenges of identifying ED concepts in text. The findings aim to support designers in better integrating design theories within LLMs for extracting ED knowledge.

This paper presents a systematic literature review to figure out challenges of integration of used components into new product generations. Reuse of components is an essential strategy of circularity and is becoming highly relevant as resources are limited and sustainability requirements have to be meet across industries. The reuse process was examined from a constructive perspective. It was found that the reuse process is not uniformly defined and that there is a divergent understanding of it. This divergent understanding continues through the Reuse process steps and the added value of using Reuse. Various technical challenges of reuse were identified. These challenges were translated into requirements that are intended to enable reuse for used components. An initial concept for solving the design problem of integrating used components is proposed with the help of these requirements.

Engineering design has recently undergone a paradigm shift led by generative artificial intelligence (AI). The Generative Design (GD) paradigm utilizes generative AI tools (e.g., large language models) to define the objective space and computationally exploit the design space. This is a drastic shift from the roles of human designers in the Traditional Design (TD) paradigm which consists of manual design-objective space co-evolution, and has created a research gap for Generative Design Thinking (GDT): how a designer thinks and cognitively approaches the design process during GD. To fill this gap, we propose the Paradigmatic Design Thinking Model which uniquely defines design thinking as situated within three factors (Design Cognition, Design Tools, and Design Methodology) and use it to explain design thinking in two paradigms: Traditional Design Thinking and Generative Design Thinking.

Understanding how Shared Mental Models (SMMs) develop within design teams has sparked interest in the design community of decades. But to date, there is still a lack of understanding of the factors that influence the development of these structures. This review examines the literature related to SMMs and the factors that impact collaborative efforts. Aiming to bring these two research fields together, this review proposes a new framework to help researchers better understand how SMMs develop and provide a foundation for new research and empirical evidence to establish the factors that influence the development of SMMs

The notion of “zero-sum” games describes situations characterized by scarcity and competition. Reframing these situations to yield more positive sum scenarios benefits from design strategies that can identify new sources of value, oriented toward out-comes that reward collective benefit over maximizing one’s individual strategy to win at others’ expense. This emergent practice, called Positive Sum Design, identifies and critiques the conditions that contribute to zero-sum bias in the interest of creatively reframing challenges and redesigning scenarios to encourage more cooperative strategies and pluralistic values. Positive Sum Design can be thus characterized as both a cultural critique and a set of creative practices applied toward that critique to help practitioners identify opportunities for transcending presumed constraints and transmuting zero sum games into non-zero-sum ones.

People rely on daily interactions with artifacts, greatly influencing their physical, mental, and social well-being. Despite this, current design practices often overlook well-being as a core consideration. Affordance theory, which explains how an artifact’s features enable specific user actions and experiences, offers a promising lens for addressing this gap. This study focuses on assessing affordance mechanisms as a potential tool to support design practices to design for positive well-being outcomes. Using transportation modes as a case study, we interviewed college students to explore how specific mechanisms can contribute to positive or negative well-being outcomes. Findings resulted in 233 examples, which showed trends in mechanisms, modes, and well-being outcomes. Ultimately, this work presents an initial framework for embedding well-being considerations into design.

This paper explores the multifaceted concept of design theories value, challenging traditional views of science and philosophy and proposing a novel framework for evaluation. Through critical analysis, considering design theories like C-K theory, PSI, GDT, and CDP, and insight from the history of science, we establish the need for a new value model of design theories that includes design-related and other general properties such as generativity, robustness, and impact on practice. We adapt a recently developed system value model (SVM) to consider the diverse perspectives of design theory stakeholders. Our framework is tested on the PSI theory, demonstrating its applicability. This paper redefines how we perceive and measure the value of design theories, offering insights that could influence future research and practice in design science.

Natural Language Processing (NLP) has been widely applied in design, particularly for analyzing technical documents like patents and scientific papers to extract engineering design knowledge. This work aims to enhance this process by integrating the Axiomatic Design methodology with NLP techniques applied to patent texts. The objectives are to (1) extract Functional requirements (FRs) and Design parameters (DPs), and (2) identify how FRs and DPs are related in text (Axiomatic relations). The second objective is particularly challenging due to limited focus on understanding semantic relations in literature, and previous studies often extract Axiomatic relations in an unstructured way. The approach achieves 60% precision for the first objective and 30-50% for the second. Moreover, a case study shows the practical application of this methodology to assist the work of designers.

Digital Twins are widely recognized as a transformative technological trend, yet their potential to foster innovation, particularly their generative capabilities, remains underexplored. This paper investigates how they can transcend traditional optimization roles to serve as tools for advancing knowledge and generativity in the design of their physical counterparts. Leveraging C-K theory, a framework is presented for modeling design processes with Digital Twins, characterizing design scenarios and identifying two distinct forms of generativity. An illustration of these results shows how designers can leverage Digital Twin reflexive capacity to challenge and reconfigure underlying knowledge of their physical counterparts. The transformative value of this reflexivity, combined with remodeling capabilities, is highlights the exploration of new design pathway for Digital Twins themselves.

Problem framing is a foundational aspect of the engineering design process, shaping how designers perceive challenges and potential solutions. Qualitiative methods, such as protocol analysis, have provided valuable insights about problem framing but are labor-intensive and time consuming. This study explores the use of a NLP technique BERTopic, to analyze framing in design conversations. BERTopic retains contextual nuances, offering a tool for uncovering the diversity and uniqueness of concepts explored by design teams while also making the analysis process more efficient. The results provide one representation of eight design group’s processes, highlighting the different and changing topic representations that emerge throughout a design session. The findings highlight the potential of NLP tools for enhancing our understanding of framing in design cognition and team dynamics.

Secure development is an ever-evolving field that has advanced quickly in recent years with initiatives like Secure Development Lifecycle (SDLC), Development Security Operations (DevSecOps), and Model-Based Security Engineering (MBSE). Despite the persistence of the security and design communities to include security in the design process, significant security breaches continue to occur. Our work reviews existing literature to determine the current state of the research at the intersection of these design and cybersecurity fields and ultimately proposes an integrative and systematic approach for developers to generate design principles that incorporate traceable security. This approach integrates security regulations and design principles and activities, encouraging compliance and security considerations at the earliest stages of the design thinking process.

This paper explores how creative preservation, affected by a regulatory framework, unfolds in the design of complex systems. Based on a case study of the Boeing 737 aircraft, it focuses on the role of grandfather rights, as part of the regulatory framework of aircraft design, as a precursor for creative preservation. The paper analyzes three design decisions related tot the evolving Boeing 737 aircraft models over a period of six decades and highlight the changing logic of creative preservation in relation to technology maturity, increasing complexity of design decisions, and expanded stakeholder involvement. Overall, the paper demonstrates that the management of design heritage is a ‘living system’ and that foundational practices may slowly become ineffective.

Design research is highly interdisciplinary, connecting to significant research problems such as the scientificity of design research and blurring boundaries of design disciplines. This paper adopts the perspective of philosophy of technology, regarding design as technical artifact-making activities. It endeavors to identify potential design research approaches based on the evolution of the philosophy of technology, and explain how these approaches have emerged, developed, and evolved. These include: analytic philosophy approach, pragmatism approach, and phenomenology approach. These three research approaches can represent the differentiation rules of design research in both independent and interrelated manners. The clarification can make the philosophical stances of technical artifact-making activities clearer, and provide philosophical references for future design meta-research.

Teams have been favored due to the diverse knowledge access. However, diversity can also have negative effects, and team outputs can be influenced by many factors, such as psychological safety. While the effects of psychological safety have been studied, its development has received less attention. Prior research in this area has focused either on specific populations or cross-sectional effects. To add to this area, this study examined the longitudinal development of psychological safety in engineering capstone students: how it evolves, and whether this can be influenced by team-related experiences. This study showed that although psychological safety did change meaningfully with time, neither time nor experience alone could capture the change. The results could shed light on the evolution of psychological safety, as well as what factors could potentially influence its development.

The emergence of new technologies, such as additive manufacturing, and places to promote access to these equipment’s, such as fablabs and makers space, has supported the development of new methodologies based on prototyping. From problem definition to customer validation, prototypes can support the different phases of the innovation process. The biggest challenge being to design the right prototype to address the objective of each phase. Here, we propose to transpose and develop a model from human-computer interaction (Houde & Hill, 1997) (Yang, 2005) to the field of design sciences. The model intends to separate design issues into the “role”, the “look and feel” and the “implementation” axes. Next, we illustrate its potential through the characterization of different prototypes fabricated within the product development process of a tool design to unbend electric pylons.

Society’s most well-intended efforts to solve sustainability challenges have not yet achieved the expected gains due to rebound effects (i.e., negative consequences of interventions arising from induced changes in system behaviour). Rebound effects offset about 40% of potential sustainability gains, but the understanding of design as a key leverage point for preventing rebound effects is still untapped. In this position paper, three fundamental scientific gaps hampering the prevention of rebound effects are discussed: (1) limited knowledge about the rebound effects triggered by efficiency–effectiveness–sufficiency strategies; (2) the influence of the counterintuitive behaviour of complex socio-technical systems in giving rise to rebound effects is not yet understood and (3) the bounded rationality within design limits the understanding of rebound effects at a broader systemic level. To address the aforementioned gaps, novel methodologies, simulation models and strategies to enable the design of reboundless interventions (i.e., products, product/service-systems and socio-technical systems that are resilient to rebound effects) are required. Building on the strong foundation of systems and design theory, this position paper argues for the need to bridge the interdisciplinary gap in the interplay of design and rebound effects, qualitative and quantitative models, engineering and social sciences, and theory and practice.

Early phases of the design process require designers to select into view elements of the problem that they deem important. This exploration process is commonly referred to as problem framing and is essential to solution generation. There have recently been calls in the literature for more precise representations of framing activity and how individual designers come to negotiate shared frames in team settings. This paper presents a novel research approach to understand design framing activity using a system thinking lens. Systems thinking is the way that we understand a system’s components and the interrelations to create interventions, which can be used to move the system outcomes in a more favorable direction. The proposed approach is based on the observation that systems as mental representations of the problem bear some similarity to frames as collections of concepts implicit in the designer’s cognition. Systems mapping – a common visualization tool used to facilitate systems thinking – could then be used to model external representations of framing, made explicit through speech, and sketches. We thus adapt systems mapping to develop a coding scheme to analyze verbal protocols of design activity to retrospectively represent framing activity. The coding scheme is applied on two distinct datasets. The resulting system maps are analyzed to highlight team problem frames, individual contributions, and how the framing activity evolves over time. This approach is well suited to visualize the framing activity that occurs in open-ended problem contexts, where designers are more focused on problem finding and analysis rather than concept generation and detailed design. Several future research avenues for which this approach could be used or extended, including using new computational methods, are presented.

This study explores how large language models like ChatGPT comprehend language and assess information. Through two experiments, we compare ChatGPT's performance with humans', addressing two key questions: 1) How does ChatGPT compare with human raters in evaluating judgment-based tasks like speculative technology realization? 2) How well does ChatGPT extract technical knowledge from non-technical content, such as mining speculative technologies from text, compared to humans? Results suggest ChatGPT's promise in knowledge extraction but also reveal a disparity with humans in decision-making.