This paper, positioned within two universities’ contexts on design education, explores the critical role of awareness in co-design with individuals who have lived experiences. The study introduces a SkillsLab designed to prepare students for managing awkward moments during co-design sessions. A SkillsLab is an intensive learning activity combining hands-on practice, theoretical insights, and practical exercises to bridge the gap between theoretical knowledge on a topic and the real-life application of this knowledge in a project-based setting. The learning activity aims to enhance students’ confidence and skills in navigating awkward moments in co-design. The findings suggest that such educational interventions can significantly improve students’ preparedness for real-world co-design challenges, fostering a more inclusive and empathetic approach to design.

Research into the foundational theories and management of software design remains limited. A 2010 workshop was convened to explore professional software development practices. The workshop sought to foster collaboration between the software engineering and design communities by examining foundational aspects of software design. Building on that workshop’s objectives, this paper investigates contemporary professional software design practice and its management within organizational contexts. It is informed by findings from three interviews with experienced software design managers. This work addresses an important gap by examining software design management through the lens of design rather than solely from a project management perspective. Additionally, it contributes to the development of a general theory of software design by integrating diverse theoretical frameworks.

The circular economy has long been regarded as a fundamental strategy for achieving sustainable development. Most recently, it has also been acknowledged as an effective approach to crisis response. This study contributes to this nascent literature by introducing a dual hierarchy of 6Rs strategies as an inspiring framework for circular post-disaster recovery and reconstruction, supporting the “Build Back Better” principle through circular initiatives. The key distinction between the proposed hierarchy and the traditional 6Rs framework lies in the two-vector operationalization of each strategy, addressing both past and future considerations. Also, this article examines the case of war-torn Ukraine as one of the most severe man-made disasters. The study explores Ukraine’s potential for circular recovery within the framework of European Union policies in the construction sector.

The advent of virtual reality (VR) enables immersive visualization, evaluation, and interaction with 3D models. Efforts have been made to integrate parametric solid modeling into VR, but efficient 3D solid model processing and intuitive user interface (UI) design remain challenging. This work proposes an architecture, which, unlike existing approaches, integrates the geometric modeling kernel Open CASCADE directly into the game engine Unreal Engine allowing standalone operation on a VR device without external hardware or software. Our prototype supports creating and editing primitives, and applying topological algorithms. 3D models in STEP format can be imported, edited, and exported ensuring compatibility with conventional computer-aided design (CAD) applications. A foundation for CAD in VR is established, focusing on a customizable UI design to enhance interaction in future developments.

Estimating consumer impressions of a product’s appearance is essential. However, this is not easy because of the variety in consumers’ tastes and differences in how consumers and designers experience design. Multimodal foundation models trained on datasets from the internet could be applicable for the estimation; however, it remains unclear if the models’ tastes are similar to those of consumers or experts like designers. Therefore, we conducted surveys in which consumers and designers rated the appearance of car wheels. In addition, a foundation model estimated the visual impression of the wheels. The model’s ratings were more similar to those provided by designers than consumers. Therefore, the models could have tastes similar to those of experts because the datasets could contain advertisements and reviews written by experts or product owners who have opinions on product appearance.

While online learning allows learners to access materials flexibly and at their own pace, many struggle to self-regulate without supervision. Real-time interventions like pop-out quizzes, screen flashes, and text warnings aim to improve attention focus but risk distracting learners and segmenting the learning process. Despite eye-tracking technology being widely used for real-time intervention design, its potential for delayed and personalized interventions remains underexplored. To address this gap, we proposed and tested an eye-tracking-based video reconstruction and replay (EVRR) method, offering targeted review at the end of online classes without disrupting the learning process. EVRR shows significant positive effects on improving learning outcomes compared to self-paced reviews, especially for learners who are unfamiliar with the concepts.

Recent advancements in machine learning (ML) offer substantial potential for enhancing product development. However, adoption in companies remains limited due to challenges in framing domain-specific problems as ML tasks and selecting suitable ML algorithms, requiring expertise often lacking. This study investigates the use of large language models (LLMs) as recommender systems for facilitating ML implementation. Using a dataset derived from peer-reviewed publications, the LLMs were evaluated for their ability to recommend ML algorithms for product development-related problems. The results indicate moderate success, with GPT-4o achieving the highest accuracy by recommending suitable ML algorithms in 61% of cases. Key limitations include inaccurate recommendations and challenges in identifying multiple sub-problems. Future research will explore prompt engineering to improve performance.

This study investigates the integration of Large Language Models with the TRIZ to improve problem solving and innovation in industrial product development. By combining the structured problem-solving framework of TRIZ with LLMs to process large amounts of data and generate ideas, this hybrid approach seeks to overcome the limitations of traditional TRIZ and optimize solution generation. In a case study conducted in an industrial setting, the effectiveness of this integration was investigated by comparing team-generated solutions with those derived using LLMs and TRIZ-enhanced LLMs. The results show that while LLMs accelerate idea generation and provide practical solutions, the additional structure of TRIZ can provide unique insights, however depending on the application context.

Computer-aided design (CAD) has become essential for hardware product development in our industrial age. However, increasing complexity, shorter lead times, and cost pressures present new challenges. While generative AI has gained significant attention and transformed various business functions, its application in engineering design with CAD remains underdeveloped. Our research aims to explore why generative AI has not yet reached its potential in CAD, despite its prominence in other fields, by identifying key challenges through case studies and a literature review. These challenges include small datasets, difficulty representing mixed data types, proprietary file formats, and lack of advanced CAD modeling commands. We propose future developments such as high-quality datasets, a vendor-neutral format, novel neural network architectures, and expanded generative methods.

Traveling on and interacting with an autonomous bus confronts disabled passengers with a handful of different and unknown challenges in terms of accessibility. To address this, a user journey was developed that includes the challenges for disabled passengers when traveling and interacting with an autonomous bus. The user journey provides a chronological list of occurring challenges for passengers with a disability. With the help of three qualitative studies in which four bus operators, ten bus drivers and 25 disabled passengers participated, the challenges of the user journey could be identified and some important requirements for possible solutions could be determined. By identifying the challenges, solutions can now be developed so that disabled passengers can travel on an autonomous bus and therefore the accessibility of autonomous buses can be increased.

Due to climate change, sustainability has become a crucial topic in product development, while addressing it is associated with many challenges. Based on a literature review, those challenges are collected and clustered into nine categories and sub-categories defined for this purpose. Additionally, a research project is analysed. The exhibited challenges such as data availability versus influenceability, a lack of unified sustainability criteria, and decision-making trade-offs underscore the need for refined methodologies and collaboration in sustainability-oriented design. The differently sourced challenges are compared and the new challenges arising from the research project are sorted into the categories. Finally, possible reasons are discussed for why within the project only challenges from four out of nine categories are encountered.

Advertisements play a key role in shaping perceptions of gender identity, which are influenced by biological traits and cultural beliefs. In India, practices like arranged marriages have historically defined gender roles, but younger generations are increasingly challenging these norms, especially through dating apps. This study examines how dating app advertisements address gender dynamics and societal challenges in India. By applying Barthes’ Semiotic theory, we analyzed a popular Bumble ad. The findings reveal how the ad promotes female agency, subverts gender norms, and portrays men as emotionally expressive. By blending modern technology with family values, the ad presents dating as empowering and respectful, challenging rigid societal norms. The study promotes inclusivity and shows how ads reshape gender narratives, and offers insights for creating socially responsible campaigns.

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 importance of the circular economy as an alternative to today’s prevailing linear economy is recognised in both industry and research. Product designers are having a major influence on this transition by adapting the characteristics of physical products in the early phases of the product development process. However, most products follow a linear approach and are far from being circular. This paper aims to identify the challenges that product designers face when designing circular products. Building on a developed understanding of related terms in circular product design, an exploratory literature review is conducted. The results help to gain an overview and understanding of the challenges that need to be addressed. Therefore, further research directions are derived to support the transition from linear to circular products in the long term.

A major cause of diagnostic errors is the underlying complexity caused by patient presentations and the context in which diagnosis is being undertaken. This is especially true for settings like emergency medicine and disease spectrums like infectious diseases. To design artefacts that counter such errors, it is essential to map the factors contributing to diagnostic complexity. However, existing complexity assessment methods in healthcare are limited in scope. Addressing this gap, our work operationalises a complexity estimation tool to identify factors contributing to the diagnostic complexity of 10 infectious disease cases in an emergency medicine setting. Our objective findings are further validated by a strong correlation with the difficulty perceived by attending doctors. The work provides a basis for the design of targeted interventions aiming to mitigate complexity in diagnosis.

As society and the field of engineering evolves, it is necessary for engineering tools to evolve as well. Through a co-design approach, this work explores the re-design of Pugh’s Product Design Specifications tool for engineering design courses to increase scaffolding of the tool for student learning and incorporate societal implications drawing upon design justice. This re-design was conducted in collaboration with Elizabethtown College faculty members, instructors and students. This paper details the iterative co-design process, showcasing the evolution of the tool that culminated in the latest iteration of the re-designed PDS tool. We conclude with a reflection on this co-design process and recommendations for evolving other engineering design tools to incorporate social justice concepts.

AI-assisted methodologies captured lived experiences and enhanced innovation practices, supporting practitioners, policymakers, and researchers in designing ageing technology. This study examined AI-assisted methods, leveraging open conversations with 30 seniors to address the complexities of ageing and technology in Singapore. Using prompt engineering, we analysed coded data with role-based, context-providing, and information-seeking prompts, generating Python code for clustering analysis. The focus was on seniors’ perceptions of technology and health concerns, revealing 25 indicators across six health dimensions. Of these, 12 social-emotional determinants influenced perceptions through emotional support and social interaction on technology adoption. Our analysis produced a four-cluster typology, providing a systematic framework to categorise perception patterns and address seniors’ diverse needs.

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.

Conformal prediction (CP) is a framework that provides uncertainty quantification output as valid marginal coverage for predictive models. At present, the main methods used are divided into Bayesian methods and statistical inference method. Among the statistical inference methods, split, full and adaptive conformal prediction are the basic methods. Although there are numerous variations of these methods, a clear comparison is lacking. In this paper, three basic conformal prediction methods are compared on low-dimensional and high-dimensional dataset to illustrate the advantages and disadvantages of each method. The experiment shows that split conformal prediction performs stable coverage but holds data partition as key issue to solve; Expected coverage could not be achieved by Full conformal though it can decrease the prediction interval; Adaptive conformal prediction faces the quantile distribution deviation of complex model. This paper also illustrate the direction of future research.

Virtual reality (VR) based product evaluation is a growing area of research, but has not yet been studied in the context of design education. In this study, participants evaluated four pairs of product design student models in physical and VR form using a custom VR application. The models included a variety of product types and a variety of prototyping materials. Participants rated the physical and VR models using a rubric adapted from a study of design student prototyping. Significant differences were found in VR versus physical ratings of some model evaluation categories, but only for certain products. The majority of participants preferred the physical model evaluation over the VR evaluation. Our findings suggest that VR product evaluation may be suitable for use in design education contexts, especially when budget or time for physical prototyping is limited.