The integration of computational thinking (CT) to enhance creativity in design students has often been underexplored in design education. While design thinking has traditionally been the cornerstone of university design pedagogy and remains essential, the increasing role of digital tools and artificial intelligence in modern design practices presents new opportunities for innovation. By introducing CT alongside design thinking, students can expand their creative toolkit and engage with emerging technologies more effectively. Although many design students may have limited experience with programming, incorporating accessible, no-code tools can help them confidently embrace computational methods, unlocking new pathways for creative exploration and innovation. This study proposes an alternative approach to improve the motivation of design students by introducing machine learning tools into product design processes. We developed an experimental pedagogy in which 56 industrial design university students learned how to use Waikato Environment for Knowledge Analysis (Weka), a machine learning tool, for three hours of design work a week, for a total of eight weeks. Our covariate analysis of data collected in the pretest and posttest shows that the general learning motivations in the group using Weka are significantly higher than those in the group without Weka. However, no significant differences were found between the two groups in terms of learning strategies, collaboration, or critical thinking. Students using Weka spent more time focusing on model training and tended to improve their algorithmic thinking, and the introduction of Weka appeared to enhance their motivation to learn. On the other hand, these students might have been focusing on working individually at their computers, potentially neglecting communication and collaboration. The findings suggest that teaching machine learning applications without requiring coding has the potential to boost design students' motivation to engage with CT skills, though care must be taken to maintain collaborative practices.

Brand style and product identity are critical to the core value of a brand. Yet how to identify the style and identity is highly dependent on the human expert’s judgment. As deep learning for image recognition has made a rapid process in recent years, it’s the application of brand style and design features have potential. This investigation assessed the car styling evolution of two car brands, Dodge and Jaguar, by training convolutional neural network. The method used heat map analysis of deep learning and was supplemented by statistical methods. The two datasets in this investigation were the car design features dataset and the car style images dataset. Results using the deep learning method show that the average accuracy of the last ten under verification modes was 95.90%, while 78% of the new cars continue the early brand style. Moreover, Jaguar had a higher proportion of style consistency than Dodge. Results using statistical methods reveal two cars had evolved in two different trends regarding the vehicle length. In terms of the design features, Jaguar had no noticeable design features of the rocket-tailfin. The heat map method of deep learning indicates a design feature’s focus area, and the method is beneficial for future brand style analysis.

In complex environments, users frequently need to manage multiple tasks simultaneously, which poses significant challenges for user interface design. For instance, when driving, users must maintain continuous visual attention on the road ahead while also monitoring rearview mirrors and performing shoulder checks. These multitasking scenarios present substantial design challenges in effectively guiding users. To address these challenges, we focus on investigating and designing visual and haptic guidance systems to augment users’ performance. We initially propose the use of visual guidance, specifically employing a dynamic arrow as a guidance technique. Our evaluation shows that dynamic arrows significantly expedite both reaction and selection times. We further introduce and evaluate haptic feedback, which users perceive as more salient than visual guidance, leading to quicker responses when switching from primary to secondary tasks. This allows users to maintain visual attention on the primary task while simultaneously responding effectively to haptic cues. Our findings suggest that multimodal guidance, especially haptic guidance, can enhance both reaction time and user experience in dual-task environments, offering promising practical implications and guidelines for designing more user-friendly interfaces and systems.

Converting biological strategies into practical design principles during the Discover–Abstract phase of the Biomimicry Design Spiral (BSD) presents a considerable obstacle, particularly for designers lacking a biological background. This research introduces a Retrieval-Augmented Generation (RAG) framework that combines a specialized AskNature database of 2106 documents with a locally executed Llama 3.1 large language model (LLM) to fill this void. The innovation of this study lies in integrating the BDS with a stage-specific RAG–LLM framework. Unlike BioTRIZ or SAPPhIRE, which require specialized expertise, our approach provides designers with semantically precise and biologically grounded strategies that can be directly translated into practical design principles. A quasi-experimental study with 30 industrial design students assessed three setups—LLM-only, RAG-Small, and RAG-Large—throughout six biomimicry design stages. Performance was assessed via expert evaluations of text and design concept quality, along with a review of retrieval diversity. Findings indicate that RAG-Large consistently yielded superior text quality in stages with high cognitive demands. It also retrieved a more varied array of high-specificity biological ideas and facilitated more coherent incorporation of functional, aesthetic, and semantic aspects in design results. This framework diminishes cognitive burden, boosts the relevance and originality of inspirations, and provides a reproducible, stage-specific AI assistance model for closing the knowledge translation gap in biomimicry design, though its current validation is limited to a small sample and a single task domain.

Positive creativity training is crucial for 21st century learning, yet the influence of affective traits and goals with different intentions on positive creativity is unclear. We held a creativity training workshop for fifty-four undergraduates to determine its influence. We first assessed participants’ affective traits (risk-taking, curiosity, imagination, and complexity) using the Test of Divergent Feeling from the Creativity Assessment Packet. Then, we provided participants with twenty-seven products as inspiration sources for designing novel staplers. Each participant was asked to define a certain design goal, for which they chose one of the inspiration sources to generate ideas. We assessed the novelty of ideas and classified them according to the goals with different intentions. Results showed a bridging role of the goals between affective traits and creativity. This role was reflected in positive correlations between (1) curiosity and novelty with effort-saving goals; (2) complexity and novelty with orderliness goals. In addition, we found participants with high risk-taking tended to set versatility goals; the orderliness goal led to the highest novelty of ideas. Our findings suggested that teachers should pay attention to students’ affective traits and guide them to set goals in positive creativity education.