We designed and evaluated an AI-based Application to enhance human creativity in design thinking workshops. The results indicated that AI hindered human creativity, resulting in fewer idea generations. The findings from quantitative and qualitative analyses comparing the only-human and human-AI teams indicated that AI contributed to the usability of ideas during the divergent phase and supported humans in converging on more novel ideas. The further development of the application is necessary to consider how humans can collaborate with AI without relying on it.

This study explores patient perspectives on hospital sustainability initiatives. Building on the Theory of Planned Behaviour, 30 interviews with patients reveal support for sustainability such as the use of reusable medical textiles, provided safety and quality are maintained. Patients view sustainability as a hospital responsibility, but value integrating sustainability communication into patient journeys. Informing and engaging patients can help shift sustainability from a background initiative into a trusted part of the healthcare system with patients as informed partners.

This study examines how different AR platforms support learning and creativity in Additive Manufacturing (AM) education. Design students used either a smartphone- or headset-based AR app to explore virtual AM models before completing a design task and questionnaire. Expert reviews and Mann–Whitney U tests showed that headset AR users reported higher usability, better AM understanding, and produced more creative designs. The results highlight the educational value of immersive AR in enhancing technological comprehension and creative performance.

This work introduces a graph-based CAD assistant that predicts the next modelling operation in parametric design sequences. Real CATIA V5 models from the automotive domain are converted into directed acyclic graphs capturing feature dependencies, enabling learning directly from structural design data. A four-layer Graph Attention Network achieved a top-5 prediction accuracy of 94%, outperforming a frequency-based non-parametric baseline. The results show that graph representations and attention-based message passing provide a strong foundation for context-aware modelling assistance.

Social robots increasingly interact with humans in diverse contexts. In this study, a systematic framework is proposed for selecting stakeholders based on the user requirements in participatory design of social robots. Matching social robot design dimensions with stakeholder fields in the framework, is achieved using Quality Function Deployment (QFD) and Design Structure Matrix (DSM) methodologies. A case study is presented to demonstrate utilization of the framework. The contribution of this paper is to develop an infrastructure towards formalization of participatory design of social robots.

Sustainability transitions in manufacturing require new competences and organisational learning. This paper presents Schedazioni, a learner-led assessment tool that helps companies analyse past sustainable design actions. Developed through case studies and a Research-through-Design process, and piloted in industry, it enables teams to map transformations, identify problems, and reflect on impacts. By shifting assessment to internal sensemaking, it supports shared understanding and strengthens sustainability capability.

Companies lack methods to anticipate rebound effects (RE) in design, jeopardising their sustainability ambitions. This action research at Beiersdorf pilots a framework for ex-ante RE identification, modelling, and prevention. The study found 31 economic, behavioural, and social rebound mechanisms triggered by a refillable packaging innovation, using system dynamics to find leverage points for prevention (e.g., foster non-msaterial practices via packaging design). This paper offers a first attempt at a practical approach to integrate RE analysis into design, towards absolute sustainability.

Cost planning for Product-Service Systems faces rising complexity, making life-cycle cost estimates essential. This paper investigates how machine learning (ML) can be applied for life-cycle cost estimation in product development. A literature review was conducted to identify ML-based methods, classify them across life cycle phases, and compare them against traditional methods. Results show that traditional models remain transparent but limited in early stages, while ML methods achieve higher accuracy in data-rich phases. A clear research gap exists for hybrid models and end-of-life costing.

This study examines how engineers formulate natural language prompts for searching existing assemblies in mechanical design. A survey with 48 engineers produced 169 prompts for different assemblies. Results show that prompts are mostly written as bullet points with an average of three and up to seven requirements. The engineers describe assemblies mainly through implicit functional descriptions and geometric or physical parameters. These findings form an empirical basis for developing generative AI-driven, prompt-based systems to foster design reuse.

Engineering outreach helps to address the STEM “leaky pipeline,” yet often lacks inclusive design guidance. This paper presents a methodology for co-designing activities using Inclusivity, Diversity, Equity, and Accessibility (IDEA) principles. Through three workshops integrating pedagogy training and storytelling, the method proved effective: participating engineers’ IDEA knowledge jumped from 20% to 75%, and delivery confidence rose from 24% to 71%. This methodology successfully equips professionals with the confidence and skills to foster a more diverse engineering workforce.

This study utilises low-cost 2D pose tracking to analyse individual technique differences in elite rowers. We established key biomechanical metrics, revealing variations linked to anthropometrics, training style and flexibility. A technique mapping tool was developed, providing objective insights that supplemented expert opinion. A pilot demonstrated the utility of this analysis to generate actionable insights for equipment personalisation. This showcases that low-cost automated methods can provide proactive and meaningful insights suitable for individualized training strategies.

Various methods are offered to measure design creativity. Nonetheless, a debate continues over the development of a trustworthy and objective method to mitigate the influences on the evaluation. Therefore, we propose and develop a novel tool and approach for evaluating design creativity, which reframes creativity evaluation as a structured and data-driven process. This tool represents a significant advancement in the evaluation of design creativity by mitigating objective factors. Importantly, our tool enables researchers and educators to adopt it freely and objectively for evaluation.

Current approaches for the generative design of sheet metal parts only take singular optimization goals into account. This paper presents a concept for a deep reinforcement learning approach to train an agent to generate sheet metal parts by combining segments from a predefined library. Through a weighted reward function, agents can be trained for different or combined optimization goals, such as weight, cost, or sustainability. The resulting agents enable the creation of a pareto front of optimal solutions, supporting efficient exploration of the design space for diverse design objectives.

Generative Artificial Intelligence (GenAI) is transforming design practice yet research lacks empirical insights into adoption in real-world design organisations. Through the case study of a European automotive OEM, we found that GenAI could accelerate ideation, but adoption was limited due to critical concerns regarding intellectual property, data security, originality, and the risk of skill atrophy. Thus, organisational capabilities like workflow specific training, transparent governance of data protection policies, and cohesive toolchains are needed for successful GenAI integration.

Electronic waste is one of the fastest-growing waste streams, yet only a small fraction is properly recycled. Many challenges in recycling originate in product design; for example the choice of materials and joining methods. This paper presents the first version of the Design for Recycling of Electronics Guide, developed to bridge the gap between design and recycling practice. Based on case studies, shredding experiments, and method reviews, it provides practical guidance to help designers anticipate and improve recyclability during product development.

This research addresses the perceptual conflict wherein consumers place greater trust in independent repair providers than in brand-led initiatives. Positioned within design for sustainability discourse, this paper, firstly examines repair service providers’ perspectives, revealing their challenges & systemic complexities. Secondly it presents their role in shaping the industry through skill transfer, community building etc. Lastly, design interventions are analyzed using systemic leverage points framework to expose varying depths of repair services to bring systemic change of fashion sector.

A parametric framework for personalised hand-wearable cooling devices is presented to optimise fluid and thermal performance using patient-specific anthropometrics. Iterative prototyping phases validated improved uniformity and efficacy. The approach bridges ergonomic customisation and thermal optimisation, enabling scalable, clinically effective wearable heat-exchangers for diverse patient populations. This study builds on prior work on personalised scalp cooling with Paxman, applying parametric principles to limb cooling for Chemotherapy-Induced Peripheral Neuropathy prevention.

This paper explores how design fiction and generative AI (GAI) were used in a master’s course addressing food consumption issues. Data from two course iterations include student outputs, reflections, and prompt records. GAI expanded speculative exploration, supporting rich, detailed futures. Two approaches emerged, exploratory and goal-oriented, highlighting the value of prompt literacy, iteration, and critical curation to sustain creative control and adaptability.