Conference Agenda

In design education, AI-assisted visualisation tools are challenging established sketching practices during ideation, influencing how students generate, iterate, and develop ideas. Empirical research on their impact on students’ creative processes is essential to guide pedagogical integration. Guided by the Design Tool Characteristics (DTCs) framework, this study analyses the survey data (n = 55) from two undergraduate design cohorts (2024–2025) using an AI sketch rendering tool during studio-based ideation. Findings reveal a central fidelity–flexibility paradox: AI’s high-fidelity outputs accelerated divergent visualisation, yet its perceived low flexibility and limited control constrained iterative refinement. This tension between rapid generation and limited creative agency challenges conventional low-commitment sketching workflows, advancing theoretical understanding of how tool characteristics shape creative engagement. Based on the findings, we propose pedagogical strategies to support stage-appropriate AI use, cultivate AI literacies, enhance creative engagement, and inform the development of AI tools that support both divergent exploration and convergent refinement.

The pervasive use of AI in design continues to increase, affecting most, if not all, facets of design practice. This is reflected in the expectations and practices of design students, even at an early stage in their education. The integration of digital tools in sketching is nothing new, but the use of AI tools may see the outright replacement of this well-established skill. This study considers the experiences and perspectives of novice design students, examining their use of AI tools and whether GenAI could replace sketching for creating images of initial concepts, assessing both against the novice designers’ intentions using a vividness of visual imagery scale. Along with examining links between designers’ own vividness of visual imagery in comparison to their education, assessing each method’s ease of use and designers’ preferences, this study uncovers various themes in novice designers’ perceptions of each method.

The use of AIGC to assist in design is increasingly common in industry and classrooms. Designers delegate critical thinking, creativity, and decision-making to AI, leading to mental inertia and blind obedience, making learners’ critical thinking crucial. This study explores how AIGC-aided E6 design thinking affects beginner designers’ critical thinking via controlled experiments. Beginners conducted "future travel products" concept design, generating sketches on LiblibAI, with the E6 design thinking scale as an intervention. Data were collected via repeated generation, retesting, and interviews. Results: 58.82% of the scale items showed improved critical thinking, 71.43% of the works were rated better by experts, and 57.14% of beginners formed "question-iteration" patterns. Thus, AIGC-aided E6 design thinking promotes beginners’ critical thinking. The study proposes future AIGC tools integrating controllability, cognitive guidance, and process iteration to aid design and foster critical thinking.

This article presents findings on the reciprocal relationship between industrial design education and the industry it prepares graduates for. Particularly considering the growing impact of Generative Artificial Intelligence (GenAI) on ideation, sketching, design methodologies, and productivity. To inform this research, industry professionals currently integrating GenAI into their practice were consulted to establish benchmarks and highlight critical areas for updated pedagogical approaches and digital transformation, thereby ensuring that design learning and productivity workflows remain relevant. The study arises from an awareness of current workforce shifts that are anticipated to become more pronounced soon. Rapid technological advancements are driving significant cultural and professional changes within both design education and industry. The traditional conception of the designer-creator and maker as the sole originator of innovation is evolving and expanding to encompass new roles as designers-curators who foster innovation through collaboration with autonomous AI agents proficient in specialised tasks with exceptional speed and performance.

Text-to-image Generative AI (T2I GenAI) tools have become commonplace in design practice and education, raising important questions about their role alongside exploratory sketching. This article explores T2I GenAI as an emerging visualisation modality that alters how designers create and explore ideas. Specifically, we examine the acute tensions between GenAI’s near-instantaneous, high-fidelity, text-driven outputs versus the ambiguous, iterative, and reflective act of traditional sketching. We report on a one-day ideation activity by 13 student designers, in which we analysed 1292 prompts, generated imagery, alongside reflective survey data to uncover patterns of productive and counterproductive T2I GenAI use. We outline prompting behaviours that characterise (counter)productive sketching behaviours, including broad exploration, reinterpretation, ambiguity and design fixation. Through our findings, we contribute practical prompting strategies to employ T2I GenAI to support and stimulate traditional sketching. As such, we provide insights on how AI can augment and challenge the cognitive and creative value of sketching.

Understanding perceived authorship and creativity is critical for novice designers to improve efficiency and engagement in human-AI collaboration. This study explores how different AI influence levels (100% = high human-drawing influence, 0% = low drawing influence) affect perceived authorship and creativity, and how these levels relate to design intents that shape authorship and support the process-oriented nature of creativity. Vizcom workshop post-surveys measured perceived authorship and creativity (N=45). Regarding authorship, when high AI influence (low authorship threshold), basic resemblance to the sketch was enough; when low AI influence, participants expected signature cues and minimal AI changes. Regarding perceived creativity, high AI influence better fosters divergent thinking in the early concept phase, while low AI influence enhances refinement. This provides a framework for when—and at what AI-influence level—AI tools effectively support design intents (loose or tight) and different creative goals (exploration or refinement), helping students strategically leverage AI tools in the design process.