This study investigates how structured co-design approaches foster team mental models (TMMs) sharedness in interdisciplinary design teams engaged in information visualization projects. Interdisciplinary collaboration faces challenges including communication barriers, diverse domains and complex informational environments that hinder shared understanding and cohesion. Drawing from literature on team mental models, team cognition and co-design practices, the study formulates four research questions related to specific interventions, integrative activities, evocative artifacts, framing guides and guided reflexivity. An exploratory mixed-methods approach involved two design teams through brainsketching workshops, with one experiencing structured co-design interventions and an unfacilitated group without interventions. Data analysis integrated qualitative verbal protocol coding with quantitative transition matrices to capture sequential interaction patterns. Chi-square analysis revealed distinct behavioral patterns, with the intervention group exhibiting richer communicative sequences. Findings reveal that integrative activities enhanced early team integration and supported divergent thinking. Evocative artifacts facilitated semantic alignment and novel idea development, while framing guides helped establish adaptive decision-making. Guided reflexivity encouraged procedural strategies around complex problem spaces. The unfacilitated group experienced difficulties, particularly in task framing and reflection processes. This research contributes a procedural framework mapping verbal activity types to team cognition stages, offering approaches for fostering TMMs in interdisciplinary design contexts.

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.

An inclusive mindset is essential for designing for inclusion. However, without a clear understanding of what an inclusive mindset entails, design educators and practitioners may find it difficult to cultivate. This study clarifies what constitutes an inclusive mindset and how it can be fostered in the design field. Through a scoping review of 47 studies, we systematically analysed research domains, types of inclusivity, definitions, associated attributes and factors influencing an inclusive mindset. The outcome is the development of an inclusive mindset model that outlines the core constructs of an inclusive mindset, its determinants and its translation into inclusive behaviour. The model was further refined and validated through interviews with 23 stakeholders in design and engineering education. Together, these insights provide a nuanced understanding of inclusivity and offer an empirically informed framework for advancing inclusion in design research, education and practice.

Current engineering design practices often overlook gender-specific needs, leading to usability deficits and safety risks – particularly for women. This paper introduces a modular framework that systematically integrates gender-specific requirements into the entire product development process. Drawing from empirical studies and existing design standards, the framework addresses four core deficits: biased data, inadequate requirement definitions, low awareness of gender sensitivity in engineering and the lack of integration in existing development models. Structured into five modules – ranging from context analysis to implementation – it offers practical tools for capturing physiological and psychosocial gender differences, aligning user requirements with inclusive design solutions and validating outcomes through gender-aware evaluation methods. Designed for compatibility with established frameworks such as VDI 2221 and ISO 9241-210, the framework enables seamless integration into industrial workflows. It supports more equitable, usable and market-relevant products while promoting diversity as a driver of innovation. Future research will focus on empirical validation and digital tool integration to further enhance its industrial applicability.

Design and innovation processes primarily synthesize the knowledge of existing technological artifacts. Understanding the foundations of such artifact-level knowledge is essential for enabling the knowledge retrieval and representation that govern these syntheses. In this study, we analyze a large, stratified sample of 33,881 patent descriptions across the total technology space. We populate knowledge graphs of these descriptions by combining factual triplets (entity:: relationship:: entity) extracted at the sentence level. From these knowledge graphs, we uncover the linguistic and structural foundations of the knowledge of technological artifacts. Linguistically, we identify syntactic patterns that explain how entities and relationships are constructed at the term level. Structurally, we identify motifs, including dominant 3-node and 4-node subgraph patterns, that reveal how entities and relationships are combined locally in artifact descriptions. Delving into these motifs reveals that natural language artifact descriptions primarily capture the design hierarchy of artifacts. At a local level within artifact descriptions, the motif analyses reveal that only abstract technical knowledge is captured, indicating potential limitations of relying on text-mining for knowledge-intensive tasks. Based on these observations, we propose and demonstrate knowledge specification strategies that can help simplify and modularize knowledge structures populated from technological artifact descriptions.

Education, including in design, is at a crossroads with the rise of generative artificial intelligence (GenAI). Although its use is exponentially growing, there are concerns that its unreflective use may undermine the development of essential skills. Regarding creativity-specific contexts, its potential to either enhance or constrain cognitive processes and creative confidence remains underexplored. This study investigates ChatGPT’s impact on ideation among User Experience Design students (N = 35), focusing on their cognitive processes, creative confidence and idea creativity. In a within-group experiment, participants generated a total of 214 design concepts under conditions with and without ChatGPT. Data collected included pre-experiment self-report of ChatGPT usage, creative confidence measures, artifacts (sketches, collages, mind maps), experts’ assessment of creativity and post-experiment self-report on creative confidence, ideas and ideation process, along with interviews. Findings indicate that while ChatGPT enhanced convenience and speed, it was associated with dampened cognitive engagement, which may result in an increased reliance on the tool and possible decreased creative confidence, potentially triggering a cycle of dependency and reduced self-efficacy, warranting further longitudinal investigation. We finalize this article with recommendations for design education to integrate metacognitive training and encourage critical evaluation of AI use, thereby empowering novice designers as active, reflective creative thinkers.

Iterations in the early stages of design can stagnate, reducing final concept quality; however, it is hard to identify these critical moments. We propose a diagnostic framework that identifies stagnation, defined as the absence of substantive changes in problem framings or solution ideas across consecutive phases, by visualizing their substantive changes/non-change as trajectories. We analyzed data collected across five-month design thinking projects, combining the visualization of 31 teams created using the framework and post-project interviews with 24 participants. Statistical analyses showed that stagnation in the problem framing was closely associated with lower creativity. A lack of change in both problem framing and solution ideas indicated a breakdown in interconnected activities between problem formulation and solution development, whereas changes in solutions alone indicated entrenchment in a single problem framing. Teams that revised both yet produced low-creativity outcomes focused disproportionately on problem-definition activities rather than solution evaluation. Teams with fixed framings but evolving solution ideas recognized the need for change yet were unable to abandon prior commitments. The proposed framework enables early detection of stagnation, allowing instructors and teams to intervene before it undermines design outcomes.

Divergent and convergent thinking are critical processes in design, ensuring that solutions are plentiful, widely applicable, original and useful. Prior research has examined the cognitive underpinnings of these types of thinking, including the relationship between cognitive load and task performance. Additionally, prior research has highlighted the impact of ADHD on divergent and convergent thinking performance and the impact of ADHD on eye-tracking metrics. However, limited research has studied the impact of ADHD on eye-tracking metrics during convergent and divergent thinking tasks, particularly in a design context. In this exploratory study with undergraduate engineering students, we aim to compare saccades, fixations and pupil diameter – proxies for cognitive load – between participants with and without ADHD performing convergent and divergent thinking design tasks. We find no significant differences between individuals with and without ADHD; however, participants, irrespective of ADHD diagnosis, exhibited more frequent saccades, shorter fixations, smaller ranges in pupil diameter and larger standard deviations in pupil diameter during the convergent thinking task. We discuss the possible implications of these differences on our understanding of creativity using eye-tracking metrics.

Understanding dexterity is a critical factor when designing physical and interactive products, shaped by the unique proprioceptive and musculoskeletal traits of users. The extent to which these individual differences manifest during physical product interactions and the methods to effectively quantify them remain largely unexplored. Measurement of subtle characteristics in hand–object interactions could assist researchers and practitioners to better understand how users interact with products, leading the way for more refined, accessible, bespoke or adaptive products tailored to individuals’ dexterity and usage. This paper investigates (1) individual differences within object interactions for single-handed highly dexterous tasks and (2) the feasibility of data-driven measurement of dexterous interaction. A study explores the ability of data-driven techniques to identify individual differences and characterise dexterous interaction, for (i) an unconstrained hand–object interaction scenario and (ii) a constrained hand–tool–object manipulation scenario. Despite a reduction in performance variance during the constrained task, the classification of user actions remained heavily dependent on participant-specific features. Models trained on group data failed to generalise to new users, highlighting the significant inter-participant variability in dexterous strategies, even under constrained conditions. Our results demonstrate that user-specific data capture could aid personalised product development and provide recommendations for implementation in future work.

Designing systems is typically uncertain and ambiguous at the early stages. Set-based design (SBD) supports alternative exploration and gradual uncertainty reduction during the early lifecycle, making it practical for complex system design. In parallel, functional requirements decomposition helps to advance the design incrementally. However, current literature on SBD lacks formal guidance on how to decompose functional requirements. To bridge this gap, we introduce a four-step method to decompose functional requirements for SBD hierarchically. We systematically define, reason and narrow the sets, breaking down the functional requirements into formal sub-requirements. This method allows parallel abstraction, ensuring the resulting system satisfies the top-level functional requirements.

This paper addresses the gap in understanding factors influencing the implementation of function modelling methods in industry practice. The study is underpinned by analysis of technical reports from workplace projects in automotive product development, focusing on three aspects: (i) the technical challenge associated with the specific workplace problem, (ii) the broader workplace context and (iii) business-focused evaluation of the impact of the method. The analysis was carried out by a mixed team of academics and industry experts to ensure robust understanding of the methodological challenges within the technical context and realistic evaluation of impact from a business perspective. The principal contribution is the introduction of a comprehensive reference framework for the evaluation of applicability of a functional modelling method in industry practice. While the study evidence is confined to the Systems State Flow Diagram method, the framework dimensions reflect generic aspects of practical application relevant to the evaluation of other methods. The proposed framework provides guidance for researchers to carry out systematic analysis of the use, effectiveness and impact of function modelling methods in real-world applications, and for industry to evaluate the applicability of methods to real-world engineering projects, including pathways for evaluating impact to justify investment in method adoption.

Artificial intelligence is increasingly interwoven with design thinking (DT), yet comparative, stage-by-stage syntheses across canonical DT models remain scarce. This literature review maps how AI augments and challenges the major stages of widely used models and relates these effects to five illustrative domains. Following the SPAR-4-SLR protocol, we searched the Web of Science (2005–August 2025), screened records in two stages and assembled a corpus of 205 eligible studies for comparative synthesis. Across models, AI scales early-stage evidence work through large-N text and behavioral analytics, widens ideation via generative systems and accelerates prototyping and testing through simulation and predictive evaluation; at the same time, risks include bias, privacy and sovereignty concerns, evaluation opacity and homogenization of creative output. The weight of evidence supports hybrid intelligence: allocate divergent exploration primarily to AI while retaining human judgment for convergent selection and ethical decision-making. A complementary AI-native “Stingray” model highlights concurrent train–develop–iterate workflows that treat AI as a co-designer, while underscoring governance needs around interpretability and auditability. Overall, the review offers a model-by-model, stage-specific map of AI’s roles in DT, along with practical guidance for responsible deployment and research priorities for assessing boundary conditions and external validity.

The evaluation of idea sets for design solutions using Shah et al.’s criteria of quality, quantity, novelty and variety can help design teams understand the thoroughness of their ideation work and can help design researchers compare the performance of different ideation methods. However, existing methods for aggregating these metrics to obtain total set scores for quality, quantity, novelty and variety are problematic. The present paper proposes axioms for the desired behavior of aggregation functions for quality, quantity, variety and novelty, then defines functions that meet the axioms. These axioms are intended to ensure that scoring methods reflect best practices in ideation and appropriately reward preferred ideation behavior, such as promoting the contribution of all ideas. Further, this paper provides operational definitions for quality, novelty and quantity evaluations of ideas and draws from previous methods to provide expedient scoring methods of individual ideas. Evaluation mechanics are presented that allow repeatable evaluation of idea sets containing thousands of ideas. Software tools are provided to automatically calculate the aggregation functions for ideas evaluated according to the mechanics of this paper. Finally, a method for evaluating both the variety of complete sets of ideas and the contributions of individual ideas to the overall set variety is proposed. The evaluation of variety is sufficiently defined that it can be automatically evaluated for any genealogy tree of ideas. The operational definitions for evaluating quality, novelty and quantity are suitable for adoption in artificial intelligence tools to allow automated evaluation of idea sets for these quantities.

The success of modern product design often relies on the thoughtful selection of next-generation technologies. However, common systems engineering methodologies tend to treat new technologies as risks to be minimized rather than as opportunities to enhance system capabilities. To bridge this gap, this study presents a new framework called PoLaRis for comprehensive technology infusion concepts assessment based on three parameters: Leap Potential, Learning and Risk. The introduction of Learning as a decision-making criterion complements Risk and Leap Potential, embedding an organizational learning perspective that values the knowledge gained through technology infusion. These three main parameters can be evaluated through expert feedback or a numerical approach. In the numerical approach, rooted in DSM analysis, Risk is quantified based on the maturity of the technology components and a system integration risk metric, while Learning is estimated from the structural complexity of the architectural changes. Leap Potential is quantified using the Technology Leap Potential (TLP) metric, which captures a technology’s contribution to product value from the user’s perspective and applies to both incremental and disruptive innovations. Two case studies were conducted to evaluate three smartwatch concepts featuring an AI power-saving chip and innovative stress detection methods. The first case study relied on 11 expert evaluations, while the second applied the numerical approach. The results showed alignment between expert and numerical assessments, indicating the internal consistency between the selected mathematical measures and expert opinions. Taken together, the Leap–Learning–Risk profiles visualize each option’s benefits and trade-offs, facilitating comparison and informed decision making.

Designing complex products increasingly requires integrative methodologies that address the rising challenges of multi-disciplinary complexity and functional inter-dependencies. This article proposes a conceptual design framework that combines the abstractional design method (ADM) with a novel inter-coupling index (ICX) to model and manage inter-component dependencies within cyber-physical vehicle (CPV) systems. The ADM provides a unified object-based representation of system components through functional and attribute abstraction, facilitating shared understanding across disciplines. The ICX quantitatively captures the degree of inter-dependency among system elements, offering a new metric for evaluating design complexity. A case study of a CPV acceleration module demonstrates how indirect coupling and cascading failure risks can be identified and mitigated in the early design process. The methodology supports the decomposition and synthesis of design architectures while preserving functional intent and reducing system vulnerability. This research contributes a transferable and scalable approach to conceptual system design in multi-disciplinary domains.

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.

Design creativity is an inherently complex and recursive cognitive process involving nonlinear transitions between distinct cognitive states. This experimental neurocognitive study provides empirical support for theoretical nonlinear and recursive models of design creativity by examining neurocognitive processes across design creativity cognitive states, including idea generation (IDG), idea evolution (IDE), rating process (IDR), and rest mode (RST). EEG signals were recorded during loosely controlled design creativity tasks, and 13 well-established features were extracted from recurrence quantification analysis (RQA). A feature selection pipeline identified the most significant features for distinguishing between the cognitive states. Statistical analyses of the features provided deeper insights into brain dynamics and confirmed the significance of the selected features, supported by EEG topography maps. The findings revealed distinct and complex recursive dynamics across cognitive states, primarily involving the frontal, parietal and central regions, offering novel insights complementary to prior EEG studies. We also classified the cognitive states using the selected significant features through six classification models: k-Nearest Neighbor, Support Vector Machine, Naïve Bayes, Multi-Layer Perceptron, Linear Discriminant Analysis and Random Forest. To ensure robust evaluation, we applied three cross-validation strategies – hold-out, k-fold and one-subject-out – and combined the classifiers using majority voting fusion. Classification results (10-fold cross-validation) demonstrated high performance, with an average accuracy (96.23%), kappa (93.56%), recall (96.58%), precision (98.08%), F1-score (97.29%) and specificity (98.43%). The study provides findings that are consistent with theoretical expectations. Consistent with theoretical expectations, the findings deepen understanding of recursive and nonlinear neural dynamics in design creativity cognition and guide future research.

Creative thinking is a crucial step in the design ideation process, where analogical reasoning plays a vital role in expanding the design concept space. The emergence of Generative AI has brought a significant revolution in co-creative systems, with a growing number of studies on Design-by-Analogy support tools. However, there is a lack of studies investigating the creative performance of Large Language Model (LLM)-generated analogical content and benchmarking of language models in creative tasks such as design ideation. Through this study, we aim to (i) investigate the effect of creativity heuristics by leveraging LLMs to generate analogical stimuli for novice designers in ideation tasks and (ii) evaluate and benchmark language models across analogical creative tasks. We developed a support tool based on the proposed conceptual framework and validated it by conducting controlled ideation experiments with 24 undergraduate design students. Groups assisted with the support tool generated higher-rated ideas, thus validating the proposed framework and the effectiveness of analogical reasoning for augmenting creative output with LLMs. Benchmarking of the models revealed significant differences in the creative performance of analogies across various language models, suggesting that future studies should focus on evaluating language models across creative, subjective tasks.

This research examines occupants’ ability to detect lighting differences as a function of proximity to the illuminated area. By understanding how proximity influences light difference detection, energy-saving lighting design techniques can be developed that do not negatively impact the appearance of architectural interiors. The experiment examined vertical surface illumination, hypothesizing that vertical illuminance difference detection thresholds would increase with greater spatial separation from the observer, regardless of gaze conditions. Illuminance was selected as the accepted metric for assessing lighting, aligning with commonly used standards, such as the European Standard and the Australian/New Zealand Standard. Eighty participants viewed a 10.0 m × 2.4 m vertical wall with five sections using a five-alternative forced choice method to identify the dimmer section. Eight experimental conditions manipulated participant position and gaze, with each subject completing 10 trials for 20 lighting conditions. Participants’ ability to detect lighting differences was very poor for wall end portions, regardless of position or gaze. Results suggest vertical illuminance in temporarily unoccupied areas can be reduced by at least 10% without affecting perceived illumination quality. Greater reductions of 25% can be achieved in room corners. These findings provide a foundation for future research into illuminance optimization across all surfaces within architectural spaces.