Over the last decade, engineering institutions have implemented changes in engineering education curriculum to address evolving industry needs. This includes the integration of design-focused curricula at various instances throughout engineering programs. This study investigates the relationship between design problem modality and neural activity. Participants in this study were engineering students enrolled in cornerstone design. Neural activity was measured using electroencephalography (EEG) during a single session where students were presented with two design problem modalities. This data was compared with motivational factors and learning preferences. The findings reveal correlations between neural activation, student motivation, and learning preferences. This suggests that problem modality influences cognition and motivation, offering valuable insight into individual student needs.

3D food printing is transforming the food industry by enabling the production of customized, on-demand foods with intricate designs. However, achieving high shape fidelity remains a challenge for optimized food ink formulations. This study investigates 3D-printed foods with overhang designs using extrusion-based 3D printing. Mashed potato and pea protein were selected as base ingredients with varied water content to assess their differences in moisture content (70–87%), pH (5.66–7.06), firmness (0.52–8.12 N), and adhesiveness (0.29–2.73 N·s). Shape fidelity was evaluated by printing geometries with overhang angles of 0° and 60°. Results showed the best printability at a 1:4 ratio (81% moisture) for mashed potato and 1:3.5 ratio (78% moisture) for pea protein. These insights provide guidelines for engineering high-fidelity food inks, that advances additive manufacturing in food design.

This paper explores the role of concept maps in investigating and highlighting project alignment through shared understanding within a multidisciplinary, long-term, creative, practice-based project called “Fish Project.” By combining surveys and concept maps, the case study investigates team dynamics, skill diversity, and evolving project comprehension. The project aims to integrate augmented reality and geo-locative technologies to cultivate care, community, and collaborative design through a digital fish ecosystem. Analysing concept map: metadata, topology, and vocabulary, the research highlights gaps in team alignment and provides areas for cohesive project visioning and execution. The findings underscore the importance of iterative communication tools to bridge interdisciplinary boundaries and strengthen team coherence.

This paper presents a motion-based taxonomy for classifying lattice structures in additive manufacturing (AM) based on their geometric suitability for linear, oscillating, reciprocating, and rotary motions. While existing classification frameworks primarily focus on static load-bearing performance, this study develops a geometry-driven taxonomy, classifying 51 lattice variations based on how tessellation patterns and wall thickness influence motion-driven deformation. The taxonomy provides a framework independent of materials, aiding the selection of lattices for compliant structures, and energy-absorbing applications, by isolating geometric tessellations to assess their role in dynamic deformation and motion suitability. This approach links lattice geometry to motion-driven behaviour, offering a predictive framework for AM design while emphasising its role in motion applications.

One focus of creativity research is the question of how creative potential can be effectively unleashed in relation to certain cognitive styles such as convergent and divergent thinking. Neurobiological findings show that different brain structures need to be activated in order to specifically stimulate these cognitive styles. By integrating tools that help understand and optimize the neurochemistry of creativity into the design process, we enable a comprehensive application of creativity and improve the ability to develop innovative solutions. In this contribution, we therefore examine which neurobiological structures undaerlay creativity and how they can be activated in a natural way. We present practical tools for fostering creativity from litertaur, make the scientific mechanisms underlying creativity accessible to designers and propose an approach for implementing the tools.

ChAx is a chatbot designed to support in technical drawing lectures by leveraging Retrieval-Augmented Generation. Addressing challenges such as the complexity of rules and dependencies in technical drawing, the system accesses the specific lecture materials to provide students with accurate and context-aware answers. The architecture combines modular components, including a RAG pipeline and a frontend with an interactive PDF viewer, ensuring transparency and user-friendliness. Optimization strategies like semantic chunking, fine-tuning, and cost-effective configurations enable efficient performance within constrained server environments. Evaluation metrics, including factual correctness and answer relevancy, were evaluated by using the LLM-as-a-judge method. The results underline ChAx’s potential to enhance educational outcomes by enabling students utilize materials more effectively.

The manufacturing process selection (MPS) greatly influences possible design decisions regarding the product’s embodiment. However, a gap remains in understanding how design engineers make these selections and what data and resources inform them. Through semi-structured interviews with engineers across various mechanical engineering industries insights into current decision-making processes are gained. The findings reveal that MPS is mostly guided by personal and collective experience, with influencing factors such as functionality and product quantities. The use of support tools remains limited. A systematic integration of data-driven tools and structured knowledge management is mostly absent. It’s concluded that reliance on experiential knowledge risks overlooking alternative processes and integrating systematic tools with existing experience-based practices could enhance MPS.

Until a few years ago, moderate alcohol consumption was thought to have (mild) beneficial effects on health. However, some recent studies have suggested that “there is no safe level” of alcohol intake. Consequently, public health institutions have responded by advising against any level of alcohol use and suggesting governments a number of policies to reduce overall alcohol consumption. Nonetheless, medical studies suffer from a variety of intrinsic limitations that could undermine the reliability of their findings, especially when focusing on low-intake levels. On the one hand, we show that the literature on alcohol consumption may suffer from publication bias; such a problem is known to be present in the scientific literature in general. On the other hand, we discuss other potential sources of bias, which are inevitable due to the infeasibility of randomized controlled trials. We assess a sample of articles for the presence of omitted variable bias, miscalculation of alcohol intake, use of linear in place of non-linear models, lack of validation of Mendelian randomization assumptions, and other possible weaknesses. We conclude that the claim that “there is no safe level” of alcohol intake is not sufficiently supported based on our current scientific knowledge.

This study presents a methodology for leveraging an LLM to generate user-centered recommendations in design for sustainable behavior. A survey of 50 users captured reasonings for evaluating thermostats’ eco-friendliness and sustainable design features. Through in-context learning, GPT-4o learned to take user perspectives for similar evaluations. The model classified 196 thermostats by eco-friendliness and design intervention types—persuasive, decisive, or both. Analysis of user sentiment and ratings of these thermostats’ reviews showed persuasive designs, which offer users behavioral control, received higher satisfaction. GPT-4o extracted features from these classifications to generate design recommendations. This method is a scalable approach for identifying user preferences and informing sustainable design decisions.

How we gather individual data to inform product design is changing. In ergonomics, methodologies are rooted in qualitative approaches, providing a holistic approach but can lack objectivity and precision. In this work, we explore novel quantitative techniques, involving machine vision and muscle sensing, to create personalized data dashboards that enrich qualitative practices in a mixed-method design. We conducted a pilot study (n=10), evaluating participants’ motion in a simple ergonomic task, followed by interviews discussing the dashboards. A thematic analysis showed that all participants agreed the dashboards affirmed their experience. Furthermore, the order of data presentation influenced their language, affecting subjectivity and specificity. This study highlights participants’ roles as stakeholders, underscoring the need for their engagement to achieve meaningful design outcomes.

The healthcare sector is a large contributor to climate change, due to their size, resource use and extensive use of single-use devices (SUDs). Despite the European Medical Device Regulation (MDR) permitting the resetting of SUDs, healthcare professionals are hesitant and seek evidence-based guidelines. This demonstration study investigates how design engineering can contribute to the feasibility of resetting SUDs that are theoretically suitable for reuse, contributing to the broader discussion on medical device sustainability. The research focuses on the quality evalualtion of reset SUDs through a detailed protocol ensuring that reused devices meet safety and performance standards. Results reveal a discrepancy between the theoretical feasibility of resetting SUD and its actual practicability. This finding highlights the necessity for more practically oriented protocols.

There is a growing consensus among philosophers that quantifying value-laden concepts can be epistemically successful and politically legitimate if all value-laden choices in the process of quantification are aligned with stakeholder values. I argue that proponents of this alignment approach have failed to argue for its basic premise: Successful quantification is sufficiently unconstrained to be achievable along multiple, stakeholder-specific pathways. I then challenge this premise by considering a rare example of successful value-laden quantification in seismology, in which stakeholder values had to be disregarded from measure design and testing. The example motivates my contention that value alignment is not a workable source of political legitimacy for successful programs of quantification.

This study explores a graph-theoretic approach to assess the alignment of R-imperatives with the integrated product development and supply chain design decisions in the transition toward a circular economy. By modeling interdependencies as a multi-layer graph, our framework quantifies alignment levels, identifies gaps, and provides strategic insights for improving circularity. The methodology employs a hierarchical matrix representation and scenario-based analysis to assess integration performance under different conditions. Numerical results from a case study in the lighting systems industry illustrate the approach’s practical applicability. Findings highlight that repair and remanufacturing exhibit the highest alignment potential, while repurposing shows limited viability. This research offers a structured assessment tool for companies to enhance circularity in supply chain management.

Well-designed products are crucial to a company's business success. Management support is a critical success factor in ensuring that design-related aspects are given appropriate attention during product development. Despite the importance of management, the literature doesn't provide a clear picture of what characterizes a competent manager in product design. This gap impedes competence development and explains why organizations struggle to leverage the benefits of well-designed products. This research aims to address this gap by synthesizing important findings from the literature into a model of managerial competence. The model provides initial insight into the individual competencies managers need to meet their responsibility for good product design in organizations.

The objective of this research is to identify and synthesize metrics to assess virtual prototypes in product design. The metrics are identified from literature and practitioners (novice/experienced designers and design faculty members), and evaluation categories are constituted. The identified metrics and constituted evaluation categories from: (a) literature and practitioners, and (b) across various practitioner groups, are compared. 144 and 29 distinct metrics are identified from literature and practitioners, resulting in 15 and 9 evaluations categories, respectively. The metrics from the practitioners is a subset of the metrics from the literature. The differences between: (a) literature and practitioners, and (b) across various practitioner groups, suggest the need for support to help practitioners choose relevant metrics for their prototyping context from an encompassing list.

Biodesign is an emerging disciplinary field that, in its multifaceted nature, finds in transdisciplinarity a promising pathway to address the complex challenges posed by contemporary scenarios. However, specific methodologies that connect the design mindset with the epistemological framework of scientific methods are still lacking. How can we grow the next generation of biodesigners in this scenario? Transdisciplinary dialog provides a foundation for merging design thinking with scientific reasoning, leading to the development of methodologies and educational strategies aimed at creating shared languages and codes that promote synergy between design and science. This study presents the results of a methodological evolution – from multi and interdisciplinary approaches to transdisciplinary ones – through a workshop focused on material design, a course designed to train future biodesigners. This workshop engaged students in collaborative material tinkering activities, working side by side with scientists in an active laboratory setting. The study demonstrates that combining a material-driven design approach with scientific methodologies fosters iterative dialogical relationships, ultimately enriching and substantiating the final design outcomes.

As Generative Artificial Intelligence (GenAI) gets integrated in design processes, building trust in these systems is critical for effective human-AI collaboration. This study introduces a framework aimed at translating the abstract concept of trust into practical strategies for design teams, focusing on four trust factors: transparency, accountability, similarity, and performance. We tested the framework’s impact on trust-building and trust learning using a mixed-methods approach, incorporating design tasks and structured workshops involving university students. The results highlight the framework’s ability to enhance participants’ understanding of trust in AI. Insights from this study contribute to advancing educational approaches for embedding trust in AI-driven design, revealing that design activities alone are not enough to impact trust learning.

This paper presents a novel framework for Artificial Creativity (AC) in design, emphasizing the co-development of problem and solution spaces. Grounded in cognitive psychology and design theories, the framework leverages advancements in artificial intelligence (AI), particularly generative AI models, to augment human creativity in design. The study identifies four key design spaces—Solution-Knowledge, Solution-Concept, Problem-Knowledge, and Problem-Concept—and defines operators that automate reasonings within and across these spaces. By enabling simultaneous divergence and convergence of problem and solution spaces, it fosters creativity while balancing novelty and effectiveness. This work bridges AI capabilities with cognitive processes of design creativity, laying a foundation for advancing artificial creativity and human-AI collaboration in design.