Conference Agenda

This article explores the integration of digital and physical spaces in Society 5.0, emphasising the role of design and AI education in addressing societal challenges through technological innovation. It advocates for an agile action research approach in design education to equip students with practical skills for real-world challenges, aligning with global trends in human-centric design. The research methodology involves a collaborative effort among 20 academics, industry professionals, and students, and in this paper, we are analysing data from two workshops. The workshops focused on understanding and aligning integration models between industry and academia. This preliminary study examined the workshop deliverables using qualitative analyses and tools like Python's Matplotlib and NetworkX libraries. It is predicated on the idea that transformative academic-industrial collaborations will be essential in Society 5.0, requiring a synergy of theoretical research and practical applications. It underscores overcoming bureaucratic and trust barriers to create sustainable, impactful collaborations. Our outcome so far is that the success of university-industry research partnerships depends on the following key factors: alignment of values, effective translation of academic research into practical applications, empathy in the context of multidisciplinary collaboration, clear communication and expectation management, and a focus on broader societal impacts. Future research will focus on integrating technologies with Society 5.0 objectives, enhancing cooperation, and reducing discrepancies between academic theory and industrial practice.

Research on the ‘materialising immateriality’ design method and the related case studies have proven that hands-on designing with textiles, by humans belonging to different cultures and nations, provides an important tactile impetus and memorable senseful experience. Based on this knowledge, we can generate innovative, resilient textile habits, and develop design didactic approaches for the younger generations, from Kindergarten on. In addition, collaborative, cross-generational and cross-cultural design doing provides resilience for the design community in terms of integration. The ‘Materialising immateriality’ design method with e.g. textile materials was developed over the course of collaborative, cross-cultural space and are showing that embodied experiences are the precondition for hacking digital tools, in designing and generating in virtual reality programs (with textile). Textile is only one example in designing with materials, architecture an other one, where first embodied experience is needed, before twice designing within digital tools, within AI will be senseful - in the meaning of designing resilient. Interdisciplinary materialising immateriality inhouse workshops are building instruments to proof innovative creating ways that we must shape our design tools with AI that will in turn shape us. And that is why hands on designing belongs relevant and as precondition for designing with AI.

The fourth industrial revolution, Industry 4.0, presents both challenges and opportunities for industrial design engineers. An European Union report on Industry 4.0 identified four main trends that will shape the future of industrial design within this new industrial revolution: new technologies, different user expectations, advancements in industry, and new laws and policies. Among the new technologies, parametric design stands out as a powerful tool for creating complex and customized shapes and structures using computational tools and algorithms. Parametric design also relates to the other three trends, as it enables personalization and customization for different user expectations, supports agile and lean production, digital transformation for advancements in the industry, and by doing so complies with the social, and ethical laws and policies. Therefore, parametric design might be considered a key domain-specific knowledge for future industrial design engineers who want to cope with Industry 4.0. Parametric design of a solution space (i.e., of potential products) and procedural thinking are facilitators to design and produce a unique or small series of products that are tailored towards specific needs and wants of stakeholders and which can be produced by digital manufacturing techniques. However, current engineering and design education often lacks the necessary teaching and learning activities to prepare students for parametric design and procedural thinking in this context in which an algorithm or stakeholder defines the product and the designer the solution space (set of rules). This paper reports on the approaches and development of educational paradigms for a course on parametric design with the Grasshopper plugin, a popular visual parametric programming plugin for Rhinoceros. The course is situated in the third year of a bachelor's program in industrial design engineering at <name of university>. The course aims to introduce students to the principles and applications of parametric design, as well as to foster their creativity, procedural thinking, and problem-solving skills as industrial design engineers. In this paper we will elaborate on the course structure based on Biggs’ constructive alignment framework, including learning objectives, teaching and learning activities, and assessment means. In this course, the students learn to define a parametric solution space in which valuable instances of the product can be created by the visual programming in Grasshopper. This also demands a mindset shift of these students, in which they design a solution space which allows a stakeholder or an algorithm to define the appropriate instance of the product. Inputs, outputs, and workflows need to be considered and are illustrated in the paper with examples. The paper also illustrates the procedural thinking, feedback statements and outcomes from the students, and discusses the lessons learned and the implications for future courses and research on parametric design education.

The ongoing information revolution has undeniably influenced the design field, which is characterized by technological advancements and unprecedented access to information. These developments have influenced designer creation and fundamentally transformed the concept of the design itself. Consequently, design practices have accelerated, offering designers a wide range of information, tools, and resources, leading to innovative approaches, methodologies, and possibilities.

This academic discussion highlights a significant shift in design practice since the early 1900s, challenging the established curricular principles rooted in the era of mass production. To meet professional and societal requirements effectively, it is imperative to recalibrate curricula to address the design field’s educational needs.

The current design tasks are complicated and require designers to possess a diverse range of skills and knowledge. Consequently, curricula must be expanded to include a broader range of experiences and expertise, encouraging designers to pursue different educational paths to meet industry demands effectively. Emphasizing a higher-level understanding of system interactions, viewed through goal-oriented lenses, allows designers to focus on specific objectives and outcomes, promoting thorough knowledge and design of complex systems.

The demands of this new era also highlight the importance of taking immediate action to address environmental concerns. Designers are encouraged to go beyond superficial environmental aesthetics and to identify tools, methods, and metrics that contribute substantively to sustainable practices.

The emergence of the product-service hybrid field emphasizes that products are integral parts of larger systems that include services and experiences. Therefore, the evolution of design education must reflect this shift by cultivating expertise in physical product design and creating associated services and experiences.

Ethical considerations play a subordinate role in the future of design education, necessitating the exclusion of ethics from curricula and principles. This integration extends beyond a mere post-production checkpoint. It requires the identification of value-oriented concerns within each discipline, thereby establishing ethics and values as integral aspects of design education.

Notably, the envisioned design curriculum empowers designers to address real-world problems through active collaboration with practitioners. By fostering ongoing partnerships with professionals in the field, design education can bridge the gap between theory and practice, facilitating robust discussions, experimentation, and adaptive improvements in response to the dynamic nature of the design practice landscape.

Computational support for concept exploration has been a research development in the last two decades, and the ascent of AI tools such as Chat GPT and generative design is further expanding it. Mechanical engineering tends to focus on more stringent functional requirements and compliance with regulation, and while the potential of AI developments to support it exists, it also likely requires significant oversight by the human. The levels of helpfulness/need for oversight and checks by humans are explored in terms of where AI could be beneficial in the mechanical engineering design process, how this could be supported, and how reliable can it be in that context. Exploring the use of AI in mechanical engineering design and the level of trust placed on its outcomes is an important question for future engineering design development, especially since AI and machine learning are improving exponentially. Exploration of students’ perception of AI and its objective usefulness will be contrasted, by performing a comparison between designs conducted with and without AI support. AI tools used will focus on product design specification generation, concept image generation and generative design. Then recommendations will be given on tools that are currently considered to be helpful for mechanical design development, highlighting the positives and negatives of the approach using AI and potential for adoption of AI in engineering design education.