Conference Agenda

Artificial Intelligence (AI) is an enduring driver for design research and practice [1, 2]; the massive potential offset with concern for the future [3]. A new reality has dawned for practice and higher education with advent of Chat- GPT [4]. The Q3 2023 Engineering Designer magazine (IED) reports ‘How Artificial Intelligence is Transforming Engineering Design: Beyond CAD’. Distinct from research agendas for Generative Design [5] and image-based AI [6], the article highlights the ‘world’s first Chat-GPT designed robot’; Lausanne researchers developing design specifications and concepts using a text-only chatbot. In the nascence of Chat-GPT, we want to understand the extent that our industrial networks and students have usefully leveraged text-only AI.

This paper reports on 2 complementary surveys on Chat GPT within: the engineering workplace and; Design Engineering HE. 58% of 120 industrial respondents agreed Chat-GPT should be integrated into university courses prompting a second student focussed survey.

Some (26%) engineers are using Chat-GPT without declaring to colleagues and with plagiarism policies referencing Chat-GPT, student use is ambiguous.

In industry the most likely (42%) application of Chat-GPT was in ‘research’, responses suggesting the tool as a “clever search engine”. This is also a critical application for students, requiring deeper understanding of differences between search engine results and the more succinct and suggestive framing of information by Chat-GPT.

43% of industrial respondents use the tool to ‘start a new task’, 18% to ‘review work’ completed by a human and a small number (7%) admitting to using Chat-GPT output verbatim. Starting and reviewing work seems likely where we will find acceptable advantages for students.

Relatively few (18.35%) industrial respondents saw opportunity for ‘creativity’, and ranked ‘efficiency’ (31.19%), work ‘scope’ (25.69%) and ‘quality’ (20.18%) as likely improvements brought by Chat-GPT. Lower ranking of ‘quality’ perhaps relates to common concerns of ‘mistrust/misuse of chat GPT’ (33.94%), ‘diminished human responsibility’ (15.6%) and the lack of concern about AI impact on job availability (1%).

Within our industrial network snapshot, practicing engineers are not using Chat-GPT to the systematic ends suggested by the Lausanne project. Early discussions with students have determined that some are using Chat GPT like industry, but more likely using the tool creatively. We expect the full survey to uncover the extent of this allowing publication of findings and implications for project-based learning and teaching in future curriculum.

[1] Herbert, S. (1969). THE SCIENCES OF THE ARTIFICIAL. MIT Press..

[2] Gill, A. S. (2023). CHAT GENERATIVE PRETRAINED TRANSFORMER: EXTINCTION OF THE DESIGNER OR RISE OF AN AUGMENTED DESIGNER. Higher Education, 2, 6.

From Proceedings of the Design Society, vol 3, ICED 2023:

[3] Müller, B. et al, BARRIERS TO THE USE OF ARTIFICIAL INTELLIGENCE IN THE PRODUCT DEVELOPMENT. p. 757-766.

[4] Chong, L., & Yang, M., AI VS. HUMAN: THE PUBLIC'S PERCEPTIONS OF THE DESIGN ABILITIES OF ARTIFICIAL INTELLIGENCE. p. 495-504.

[5] Thoring, K., et al., THE AUGMENTED DESIGNER: A RESEARCH AGENDA FOR GENERATIVE AI-ENABLED DESIGN. p. 3345-3354.

[6] Brisco, R. et. al., EXPLORING THE ROLE OF TEXT-TO-IMAGE AI IN CONCEPT GENERATION. p. 1835-1844.

Higher education institutions (HEI) are facing fundamental questions regarding students’ use of artificial intelligence (AI) tools in the form of large language model (LLM) based chatbots. Students are already using AI tools to respond to written assignments and exams. Our research question is: What is educators’ standpoint about students’ use of generative AI in higher education? A mixed methods approach was applied for the present study. First, a qualitative investigation was conducted, centered around interviews that revolved around potential consequences (i.e., opportunities, threats, challenges, etc.) and factors related to the educators’ views on AI. Based on the qualitative approach, three propositions were postulated for a narrower quantitative approach, including a larger sample of educators from industrial design (ID) educations at HEIs’ in Europe. The quantitative data was collected through a questionnaire and analyzed using a fuzzy-set qualitative comparative analysis (fsQCA). The findings from the questionnaire supported our proposition about (1) Knowledge about AI leads to seeing opportunities rather than challenges, but not our propositions of (2) Emphasizing skill-focused learning outcomes leads to seeing opportunities rather than challenges, and (3) Use of authentic cases leads to educators’ not emphasizing challenges. This study emphasizes the importance of knowledge about AI for educators.

To cater for students’ dire need of writing instruction in the transition from upper secondary school to higher education, Norwegian universities have established writing centers as part of their general library services. However, university teachers still experience that students are struggling to meet requirements in their distinct courses and programs. In research on academic writing in higher education there seems to be a vacancy: Academic writing instruction in the disciplines.

The students’ unpreparedness for academic writing is an acknowledged problem in higher education entry courses and programs. Students are expected to demonstrate their knowledge in written assignments and examinations but are not sufficiently prepared for writing in their respective disciplines. The array of writing courses offered vary from discipline-specific courses to the more general writing courses the libraries offer. The effects of these courses are rarely measured.

This article evaluates the use of large language models (LLMs) as learning assistants alongside with academic staff.

The academic writing instruction was prepared in collaboration between university teachers and the library’s writing center. The aim was to evaluate and obtain understanding on how to use LLMs effectively in academic writing instruction. A survey was conducted to research students' experiences with LLMs as academic writing instruction, and how staff’s instruction can be improved.

The results illuminated that LLMs can be powerful tools alongside with academic staff instruction when students are trained well in using them efficiently. Some students reported however, that using LLMs for academic writing was more time consuming that first expected. They still found teaching and supervision from staff useful, both to achieve the learning outcomes for the course, but also for use in other writing situations in their education. Still, LLMs seems like a useful tool for writing supervision.

This paper sets out to interrogate the legitimate concerns and issues around the rights and ownership of AI-assisted and AI-generated work in the future. The intention is to map out the current debate and legal frameworks to determine what the new frontier of intellectual property rights may look like as we cross into the uncharted landscape of artificial intelligence and its implications for creativity. AI presents us with both societal altering opportunities and challenges. The often-dystopian representation of advanced AI in popular culture may well colour our perception of its potential and power. However, we are now at the dawn of an era where AI will undoubtedly impact on each and every citizen on the planet. Its potential for good and bad is increasingly being discussed and debated against a background of many other profound challenges of our time. Arguably the ability for humans to make good and appropriate choices regarding their creative and imaginative interventions in the world remains questionable. Will we be any different with AI interventions? With that in mind, the focus of this paper is to consider and interrogate the nature and effect of ownership of AI both now and into the near future.

The question of intellectual property rights and ownership around AI and its outputs is likely to be both disruptive and contested. Its integration into the everyday lives of citizens is increasingly ubiquitous and authorities are struggling to find ways to regulate both its application and ownership. Those in control of AI will wield enormous power and influence, for good and for bad. Even before we address the question of the impact of ownership around AI, we must acknowledge that Intellectual Property (IP) itself is contested in terms of its control and value. However, in general there is acceptance of the legal framework that protects intellectual property under the guidance of the United Nations World Intellectual Property Organisation. It is generally agreed that Intellectual property is that property that emanates from the creations of the human mind. The purpose of intellectual property rights and protection is to give the creator an exclusive right over the use of their creation for an agreed period of time to enable them to accrue some technical or economic benefit from their creation or innovation. Intellectual property rights therefore enable and support an ecosystem of creativity and innovation that drives cultural, scientific and technological pursuits. AI may well pose a threat to this ecosystem. Legal and ethical concerns have begun to emerge as to whether generative AI programs may infringe copyright of existing works. Further concerns arise as the discussion embraces the possibility of AI itself being granted Intellectual property rights. The issue of AI creation, authorship, and inventorship has implications for global IP policy.

The issues raised here may have considerable impact on both Engineering and Design Practice and education in terms of how we engage with and exploit the benefits of generative AI without losing the integrity and motivation of the human creative endeavour.

The appearance of engineering design methods has evolved from drawing boards and blueprints to CAD screens and possibly augmented reality. Has decision making evolved, or is artificial intelligence (AI) unlikely to supplant the disciplines and practices adopted in engineering design teaching?

AI speeds and extends the processing of communications to simulate responses from natural language inputs, also creating graphical responses by emulating the widely sampled rules inferred from graphics. Can it go further to synthesize decision making in form and material? Certainly entertainment industries create virtual worlds from combining an understanding of creative intent and multiphysics. Generative design distributes material according to rules for both structural performance and manufacturability. Can distributed computing begin emulating expertise applied to optimise utility, aesthetic and tactile appeal? If so, what shall engineers be able to add, that can be taught?

It may be misleading to extrapolate from the past, but some reverse predictions may ring true. Leonardo Da Vinci sketched and today’s engineering designers still sketch – we still (should) teach sketching through practice. Do computers read their own and other’s sketches – not yet.

IDEO design novel solutions by bringing creative insights together – not mechanistically but through human dialogue, while walking in the shoes of users and other stakeholders. We still (should) teach requirements capture and consultation throughout iterative development. Does ChatGPT start to enquire about the welfare of users and show understanding of what makes them productive, healthy, happy and occasionally delighted – not yet.

Manual skills have been applying the craftsmanship we take for granted at a macro scale since cathedrals were first raised far higher than many grand designs, though weaving micro and nano scale solutions is clearly the preserve of automation. The human feel for manipulation materials is innate, hard to copy by machine learning. Though combinations of manufacturing processes get quicker and more accurate, will they sense their outputs, experiment serindipitously and fail often – not yet.

Clearly we already trust simulations to tell us whether combinations of materials and geometry will fly, float and house our children’s children in the conditions we foresee. Some simulations can also experiment at the molecular level and even explore what will grow, around and even within us.

However we have not yet learned to implement many of the proven solutions to some of the biggest environmental, economic and social challenges.

To equip new generations of engineers, shall we perhaps retain our belief in the tools we trust – a sketchpad, a desire to curiously seek first to understand (before being understood) and a healthy workshop to make, break and tinker.

To learn to engineer is to learn to control a very small part of the physical world. Then how sustainable the combined effects become is up to us and our retaining the practice of learning from continually examining our methods and results. AI can accelerate our improvements, though let's tread softly and carefully.

As the process of discovering, defining and solving problems, industrial design activity represents the designer's thinking cognition and innovation ability. However with the emergence of artificial intelligence (AI)technology, there is a potential for designers to incorporate unintended problems into their design solution. For example, a false information, or bias error and ethical imbalance can infinitely amplified through computer coding incorrect design solutions. If the teachers have poor understanding of the AI limitations this may have devastating impact on the development of future industrial designers. This paper surveyed Industrial Design teachers. The survey aimed to explore the Industrial Design teachers’ understanding of AI role in the design process. The following questions guided this study: How do teachers envisage the use of AI by their students? Do they think that the AI may affect students' creative behaviour during the design process? First, we reviewed literature to understand the AI potential to inform industrial design activities. Then we examined the feasibility of AI intervention in the design innovation process. The results show that AI, as a design tool, can facilitate industrial design students design solutions faster. Nevertheless, the AI has not provided students with learning opportunities and development related to creativity.