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.

Despite the increasing environmental awareness, sustainable products are not routinely adopted, revealing a persistent attitude–behavior gap. While this gap has been widely studied through psychological and behavioral approaches, less attention has been given to how product design attributes shape users’ perceptions and influence their interaction with products. Therefore, this article aims to explore the literature on the role of design attributes in this context, analyzing how product design attributes and sustainability cues affect user experience and product-related decisions. Based on a systematic literature review process, 52 articles were extracted from the SCOPUS database. These articles were classified and analyzed according to their main objectives. Based on the findings, attribute centrality (i.e., whether a product feature is perceived as central or peripheral to the product’s identity) and product category (utilitarian or hedonic) are relevant to shape environmental attitudes and behavior. Specifically, sustainability cues tend to be perceived more negatively in utilitarian products, where functionality is prioritized, and more positively in hedonic products, where they may enhance emotional value, especially when perceived as central attributes. Therefore, these characteristics must be strategically integrated in product design to reduce the perceived trade-off between sustainability and performance. The review conclusions highlight the need for further investigation to identify how specific product design attributes resulting from the application of ecodesign principles trigger sustainability perception.