Behavioural design processes have proliferated in recent years across a diverse set of fields including policy, product development and health. However, this diversity of perspectives also increases ambiguity regarding which (and when) processes should be enacted, which hinders research and practice across fields. This drives two research questions: (1) How are behavioural design processes currently framed, described, and enacted? and (2) How can we consistently understand commonalities and differences across behavioural design processes? In response to these questions, we adopt a critical interpretive synthesis (CIS) approach, reviewing 12 processes from academic and practitioner sources selected through purposive sampling and analysed using a theory-informed coding protocol. Through interpretive synthesis, we re-characterise behavioural design in terms of an ecosystem of distinct but complementary processes rather than its typical presentation in fixed sequences of steps. This increases behavioural design’s ability to respond to different degrees of uncertainty and dynamism in the problem and solution as well as its ability to reflect diverse assumptions about uncertainty, iteration, outcomes and practitioner capability. This research supports an important and developing interdisciplinary area by bringing design process into a design science research context through which many of these topics can be further discussed and developed.

Engineering design applications that emphasize positive societal impacts are growing in popularity, yet often overlook the critical importance of engineering designers’ and stakeholders’ positionalities – their unique identities, experiences and resulting perspectives and social positions relative to others – in shaping design decisions. Insufficient attention to positionality can limit designers’ abilities to navigate complex problem contexts, engage diverse perspectives and address power dynamics, ultimately constraining the effectiveness and equity of design outcomes. However, little is known about how designers conceptualize and account for positionality in practice, particularly in the early stages of design when problem framing decisions are made. Therefore, this study explored how 10 engineering students and 10 practitioners conceptualized positionality in the initial stages of design for “social good,” where its impacts are especially pronounced. Each participant engaged in a written reflection and semistructured interview. Key findings include limitations in participants’ available language and strategies for accounting for positionality in design processes, particularly in the early stages, and that participants’ learning about positionality was largely driven by exposure to diverse identities and contexts. These insights highlight the limitations of engineering training and skillsets in design-for-social-good and emphasize the need for strategic, intentional consideration of positionality in design practice and education.

This research proposes a systematic method for design ideation with large language models (LLMs), grounded in the design operation model inspired by Christopher Alexander’s pattern language (PL). Design operation refers here to a tree of thought for the step-by-step definition of the design context behind a design problem, the functional requirements, the physical attributes of alternative solutions and the generation of design alternatives that synthesize those attributes. To examine how the design operation improves LLM performance for design ideation, we implemented an architectural design ideation case study and compared four prompt methods for generating design alternatives. The four prompt methods are designed with and without the design operation and the PL. Respective prompts generated 100 design alternatives which were evaluated both by human experts and through computational diversity metrics. The results showed that prompts using the PL tend to generate design alternatives whose creativity is highly rated by humans, but are strongly influenced by the given knowledge and lack diversity, whereas prompts incorporating the design operation have the potential to enhance validity and feasibility and attribute diversity.

In safety-critical industries such as aerospace, managing uncertainty is important, both to ensure airworthiness and to control business risk. Although design margins are widely used to support safety, reliability and regulatory compliance, they are often applied conservatively, with rationales that are implicit, inconsistently documented or unevenly interpreted. While the use of margins is necessary from a safety, reliability and regulatory perspective, excessive use of margins can lead to undesirable effects such as overdesign, consequently leading to heavier parts and therefore design inefficiencies. While the role of margins in this context is well appreciated, there is a gap between the theoretical understanding of margins and their use in practice, especially in relation to uncertainty. This paper investigates this gap through a qualitative study involving 11 in-depth interviews with experienced engineers and managers at a leading aerospace component design and manufacturing company. The interviews explore the current industrial practices, cultural barriers and decision-making heuristics surrounding the practice of design margins. The findings reveal a reliance on legacy practices and tacit knowledge, which may limit margin transparency and potentially contribute to margin stacking. We conclude with actionable implications for the proper documentation and use of margins in engineering design.

Designers recognize the importance of human values in shaping user experience and meaning-making. While existing approaches acknowledge the role of values, they often lack a clear integration of empirical grounding, theoretical coherence and practical usability. This paper proposes a double triangulation design research methodology for developing value-oriented tools. Rather than treating triangulation solely as a validation technique, it is a generative research logic that integrates empirical data, theoretical perspectives and iterative design experimentation. The approach is demonstrated through the development of a design tool created to support designers in identifying, articulating and reflecting on human values throughout the design process. The study combines a literature review, a human values survey (n = 568, 69 nationalities), theoretical grounding in Dooyeweerd’s modal aspects and iterative tool development and evaluation. Empirical analysis produced alternative value structures, which were further refined through design experimentation and applied in a quasi-experimental study with design students. The results show that triangulation enables the coevolution of value frameworks and design artifacts, allowing abstract value concepts to be translated into actionable design resources. The main contribution demonstrates how triangulation can be used as a structuring logic in design research to develop evidence-based tools.

Behavioural design is an important, emerging area of research and practice. However, major questions remain regarding how behaviour change mechanisms should be embodied in artefacts to create effective interventions. Hence, we ask: How do behaviour change mechanisms and their embodiments emerge and relate to each other during behavioural design processes? We answer this question via analysis of 11, in-depth case studies of expert practice. Based on our findings, we reveal a complex, multi-layered co-evolutionary process in behavioural design, capturing development within and between spaces linked to behaviour change mechanisms and their embodying artefacts. Further, we relate the structure of this co-evolutionary development via process synergy between mechanisms and embodiments spaces to understanding and cohesion of the final interventions. This has significant implications for behavioural design theory and process guidance, as well as highlighting the need for further research on the link between design processes and interventions in this context.

This study investigates how tools derived from design theory, particularly C-K theory, can be used to foster creativity in children by mitigating cognitive fixation in school settings. Conducted with 123 children aged 8–11, the study compared four pedagogical methods: use of fixation examples, use of expansive examples, brainstorming and a novel “defixation” intervention based on C-K theory. Over a 5-week intervention, children participated in structured creative workshops aligned with the school curriculum. Creativity was assessed through fluency, flexibility, fixation and expansion scores during idea generation tasks, including a pre- and post-test using the egg-drop task. Results showed that while example-based methods increased idea fluency, they also reinforced fixation and reduced flexibility. Brainstorming led to high fluency but did not significantly enhance originality. In contrast, the C-K-based intervention successfully increased both flexibility and the number of expansive ideas. This research demonstrates the potential of design tools in education, offering a scalable and theoretically grounded approach to enhancing creativity in classrooms. It also underscores the limitations of commonly used methods, such as brainstorming and example modeling, when used without attention to fixation dynamics.

Responsible design requires anticipating uncertain, systemic and distant consequences, despite cognitive limitations in reasoning beyond familiar practices and immediate effects. This paper presents AutoVision, an anticipatory design platform supporting the generation and evaluation of autonomous vehicle (AV) concepts. It combines the Study–Method–Impact (SMI) model, which structures scientific evidence on long-term socioeconomic and environmental impacts, with the Representation–Usage–Impact (RUI) model, which captures expert knowledge on social and ethical consequences. Through its Advisor and Impact Guesser modes, AutoVision links impact-oriented objectives to design directions and concepts to traceable impact pathways. We evaluated the platform with eight senior designers from an automotive group to examine its relevance and the cognitive limitations affecting responsible concept evaluation. Participants created and evaluated AV concepts, including one generated through AutoVision without knowing its origin. The AutoVision-generated concept was perceived as at least as convincing as the designer-generated alternatives. Participant judgments diverged from the platform’s evidence-supported evaluations, particularly on environmental and ethical issues, while their justifications covered a narrower and less nuanced range of consequences. These exploratory findings show how anticipatory support can complement designer judgment, foster reflexivity, and help address cognitive limitations in responsible concept design without claiming to predict future impacts.

The use of digital twins in engineering design promises enhanced performance through real-time simulation and integration of cross-disciplinary knowledge. However, their potential to support creative exploration – fostering novel and unexpected solutions – remains underexplored. This study fills this gap by applying C-K design theory to an analysis of 16 digital twin projects conducted in hospital settings, focusing on airborne contamination challenges during and after the COVID-19 pandemic. Our findings reveal an original way in which digital twins can be used to support creative reasoning. By qualifying the types of knowledge generated through their use, we show that, beyond refining and enriching the information available to designers, digital twins can also produce results that encourage designers to question their initial assumptions. These insights challenge the prevailing view of digital twins as mere optimization or validation tools. Under certain conditions, they can also foster creative reasoning, conceptual expansion and problem reframing. Overall, our results underline the need to more closely integrate the design of digital twins with their actual use, and point to a potentially expanded and enriched role for the designers of these technologies.

Testing is often narrowly viewed in the context of verification and validation in product design and development. However, testing also plays a critical role in product design, especially in the early stages, for exploration, experimentation and evaluation. While agile and set-based design emphasise iterative testing, these approaches do not fully address testing purposes across stages or consider diverse stakeholder needs. Empirical studies examining the multifaceted roles of testing throughout the design process are scarce in the academic literature. This paper fills this gap by presenting two detailed case studies on engine design and space propulsion systems, covering both incremental and radical product development in large-scale manufacturing settings. The findings show how testing purposes evolve with design progress and increasing specificity. Drawing on insights from the case studies and a review of the existing literature, the paper develops a systematic categorisation of the purposes of testing across design and development stages. This provides a foundation for a shared understanding among diverse stakeholders to support the effective use of testing in practice and offers consistent terminology to connect the academic literature on testing.

Empathy is essential in understanding the user’s experience. However, the relationship between the different empathy constructs, such as empathic mental processes and empathic understanding (EU), remains unclear. Therefore, in this study, we explore EU from both the designer’s and the user’s perspectives and how it relates to empathic mental processes. Specifically, we investigate the relationship between the user’s perceived empathic understanding (PEU) and the designer’s actual EU. We also examine the role of mental processes, specifically verbal mimicry, measured as Language Style Matching (LSM), in developing EU during interviews. The results show a relationship between PEU and EU, but no correlation between verbal mimicry and either perceived or actual EU. However, our results show that verbal mimicry indicates the user’s expressiveness during the conversation, which may facilitate the relationship between PEU and EU. We find that when both mimicry is high and the user perceives the designer as empathic, the designer’s EU is almost always high, and vice versa; however, this is less predictable in interviews with low mimicry. We conclude that treating empathic mental processes as a prerequisite for developing and attaining EU might be more useful than investigating a relationship.

Undergraduate research experiences (UREs) are believed to benefit students, but there is little concrete evidence to support this claim. Likewise, faculty often see UREs as a burden, yet multiple studies show UREs benefit faculty. Outcomes could be improved for both parties if projects were designed better. We report a rational design and decision-making framework for project-based mentoring experiences like UREs to address this misalignment by helping mentors balance competing research and learning requirements. The method requires the mentor to identify factors that are important for success and evaluate their project on those factors. Responses are analyzed using possibility theory to account for uncertainty in these judgments. Three projects are analyzed as a demonstration. We show the method can be used to rank projects based on predicted outcomes and identify areas for improvement, and we describe a procedure for iterative design as evidence changes. Applying the tool to UREs, we show that the method is capable of identifying inconsistencies in the mentor’s beliefs and provides a clear and straightforward procedure for introspection and adaptation over time. More broadly, the method and analysis could be adapted to design and analyze other project-based experiences under uncertainty.

Creativity in design is assessed based on novelty and value. Existing methods to assess novelty, value and creativity (NVC) in design primarily focus on ideas/concepts. The objectives of this research are as follows: (a) to develop a method to assess novelty and value of prototypes and, from these, calculate their creativity; (b) to demonstrate the use of the method and (c) to evaluate the method’s precision, reliability and effectiveness. The method uses metrics and evaluation aspects pertinent to a given prototype, and these are extracted from requirements in a design task. The method is demonstrated by using it to assess NVC of functional prototypes developed in a course project. The method’s precision is evaluated in terms of variance in NVC values when used to assess a given functional prototype; the coefficient of variation ranges between 0.22 and 0.44. The reliability is evaluated by comparing NVC values of a prototype obtained through the method with those estimated by experienced designers; the normalized RMSE varies between 7% and 16%. The effectiveness is evaluated by comparing NVC values of a prototype assessed with/by: the proposed method, an existing method and experienced designers. The proposed method shows more alignment with experts’ intuitive evaluation compared to the existing method.

The increasing importance of co-creative tools in creative graphic design tasks has made it essential to understand how different levels of expertise shape the collaborative process between humans and computers. This paper presents a study with 30 participants to explore the impact of the PatternPursuit on the creative process, as well as on the collaborative experience and the user’s perception of the system as a creative agent. PatternPursuit is an ideation system that uses pre-digital design work to produce graphic patterns for wrapping paper. The study involves two different groups of users interacting with the system; one group comprises design experts, and the other comprises non-experts. The results suggest that experts tend to see the system as a creative partner that challenges their objectives, while non-experts see it as a tool that broadens their skills and supports creative exploration. The findings can be used to develop effective co-creative systems that consider the target audience and their understanding of the creative process.

Learners’ Design Competence (LDC) is the ability of the learner to integrate and apply relevant knowledge, skills and attitudes to effectively perform a given design task in more than one context. This article aims to characterise this design task, specific to its context, so that it may be administered to assess the design learner. A comprehensive review of the literature suggested a suitable model of design competence for its validation. To begin with, its construct was validated using the Kipling method to check if it can identify the primary (sub)factors that determine a design task. This has led to the proposal of a design task ontology. Next, the comprehensiveness of its content was validated with the thematic analyses of empirical evidence by comparing the Inter-Coder Reliability and Krippendorff’s alpha (K-α) against the ground truth, which suggested revisions in the ontology. Finally, the criterion validity was assessed by defining the relationships among its factors through a design task syntax. This validated its application in real-life situations. The complete set of design tasks thus framed were reposited in a design task generator tool to facilitate the statement of design tasks necessary for assessing LDC.

Design computing research, a subfield of design science, focuses on developing computational frameworks and tools for design-related activities. Growing interest in the field has resulted in an abundance of academic resources. To help researchers utilize these, we enumerate and organize the research questions pursued across design computing research, regardless of a specific design discipline or method. An existing taxonomy derived from a single venue (DCC) was applied to the recent proceedings of two additional venues (E&PDE, CAADRIA). This process has yielded a revised taxonomy that can account for the recent proceedings (2024) of all three conferences (N = 253), thus demonstrating potential for generalization. The taxonomy classifies works in the field into three major categories: (A) explanation of design-related activity, (B) application of knowledge for enhancing design-related activity, and (C) organization of knowledge on a meta-level. For each category, the corresponding questions and sub-questions are listed, and example papers are provided in the form of an index. A complementary computational exploration of clustering using machine learning has further highlighted the potential usefulness of the taxonomy. These categories can help researchers position their work in a broader context, support design computing pedagogy and help to create a map for the field.

Prototyping is an integral component of the engineering design process. Despite its central role, there remains a gap in understanding how prototyping frameworks might overlap, align or contradict, which this systematized literature review addresses. Understanding this gap is crucial, as prototypes directly influence the efficiency and effectiveness of developing new technologies and products that shape everyday life. This review synthesizes research from engineering design by examining theoretical and empirical studies, providing a holistic perspective on the current state of prototyping scholarship. A preliminary framework presents four aspects that can be used to characterize prototypes (Purpose, Dimension, Representation, Approach). Findings reveal that literature on virtual prototypes appears distant from literature on iterative and parallel prototyping. In addition, there are fewer pieces of literature on virtual, augmented and mixed reality focused on building conceptual or theoretical foundations. Clustering analysis reveals relationships and trends within the current literature. Finally, an in-depth examination of hybrid prototyping highlights inconsistencies in the way it is defined, underscoring a lack of consensus in the engineering design community. The outcomes of this review aim to clarify ongoing discourse in engineering design theory, call for expanded experimental work and present four propositions to be examined by future work.

Design methods should support engineers in efficiently developing technical systems but are often considered difficult to understand and apply. Conventional method validation focuses on performance outcomes, providing little insight into how users experience methods during application or adaptation. Structured reflection has been discussed to uncover misunderstandings and identify improvements, but its systematic application in method development remains underexplored. This article investigates how reflection can support design method adaptation in early validation. A multiple-workshop case study was conducted with 20 participants applying four methods under comparable conditions during the Product Architecture Design Summer School. Reflection was systematically planned, guided and analyzed through coded audios. The dataset includes four planning documents, 20 Canvas documents, 400 minutes of recordings and questionnaire feedback on reflection tools. Results show recurring reflection patterns: reflection is strongly process-oriented; action planning emerges only after descriptive phases; social aspects are rarely addressed without explicit prompts. While reflection tools were perceived as useful, guiding questions require simplification for intuitive use. A four-stage framework for Reflection-Driven Method adaptation is proposed, supporting researchers in planning, executing and analyzing reflection as a method validation instrument. The study demonstrates how reflection can produce actionable improvements and offers a reproducible approach for early method validation in engineering design.

This study explores the task-dependent effects of ChatGPT support in synchronous collaborative Computer-Aided Design (CAD) activities. While large language models (LLMs) such as ChatGPT are increasingly being integrated into engineering design, evidence on their influence in collaborative CAD activities remains limited. Addressing this gap, a between-subjects experiment was conducted with 22 pairs of engineering students performing three synchronous collaborative CAD tasks requiring varying combinations of CAD and domain-specific knowledge. To assess the task-dependent effects, the study evaluated taskwork through the number of CAD user actions, productivity, efficiency and CAD dominance. Results show that ChatGPT’s influence was highly task-dependent. In tasks relying primarily on CAD knowledge, ChatGPT-supported pairs exhibited lower productivity and efficiency. Conversely, in tasks requiring domain-specific engineering knowledge, ChatGPT support showed potential to improve efficiency. The study also offers methodological insights for designing controlled experiments involving ChatGPT support in collaborative CAD, underscoring that effective integration depends on task, tool and user-related factors. The findings further imply that integrating ChatGPT into engineering design education and practice requires developing competencies for critical and coordinated ChatGPT use within collaborative workflows, ensuring that ChatGPT complements rather than replaces designers. Future research should examine professional settings, teamwork and long-term adaptation to ChatGPT-supported synchronous collaborative CAD activities.