FUTURE PROOFING COMPUTER-AIDED DESIGN EDUCATION THROUGH AI-DRIVEN E-LEARNING
Year: 2024
Editor: Grierson, Hilary; Bohemia, Erik; Buck, Lyndon
Author: Kravchenko, Olga; Green, Stephen
Series: E&PDE
Institution: Imperial College London, United Kingdom
Page(s): 336 - 341
DOI number: 10.35199/EPDE.2024.57
ISBN: 978-1-912254-200
ISSN: 3005-4753
Abstract
Since the emergence of Computer Aided Design (CAD) software in 1957, educators have been committed to equipping design engineering students with proficiency in the most widely employed CAD tools in the industry. In the early days, training focused on step by step instructions and understanding CAD theory. Overtime, lecturers adopted more hands-on practice, project-based learning and collaborative learning techniques [1]. The abundance of online resources and tutorials has allowed lecturers, particularly in Higher Education (HE), to focus less on training and more on theory and use of various software and techniques. Over the last decade, the number, pace and emergence of unique sub-sector software development has grown exponentially, making it difficult for educators to adopt a fixed CAD curriculum. In the early days, the dominant players in the CAD software arena until the early 2010s were Solidworks, Rhino 3D, and Autodesk Maya, each equipped with its own proprietary rendering engine. However, over the past decade, there has been a proliferation of CAD software alternatives, complemented by independent rendering engines like Keyshot, notably embraced by software applications such as Blender, Gravity sketch, and Sketchup. In particular, Blender and Rhino's Grasshopper add-on have experienced a surge in user-generated custom add-ons, broadening the array of available features and functionalities. The rapid expansion of Artificial Intelligence (AI) tools over the last two years has introduced a new layer of complexity, as tools such as Midjourney, Stable Diffusion, and Control Net have gained prominence in the industry. The exponential growth of computational tools available has presented higher education institutions with a perplexing choice regarding which CAD software to teach and the most effective pedagogical methods [2]. This complexity is further compounded by the dynamic nature of software development and the diverse career paths of students, each demanding a distinct skill set for success. This intricate situation prompts the inquiry into an approach to future-proof and tailor CAD software education to the unique requirements of each student cohort . Perhaps the same tool that advances learning can be used for future-proofing? Whilst there are examples of successful uses of AI in education in other disciplines, the inquiry using baseline data from undergraduate and postgraduate students at a world leading institution raises additional questions: • Can we design an AI tool that develops bespoke CAD learning for students? • Can this learning go beyond simple command instructions and extract higher universal principles of CAD theory? • Specifically in HE, can AI be used to create individual learning plans based on student’s interests, industry needs and ongoing advances in CAD? References : [1] Brink, Kilbrink & Gericke, 2023. Teach to Use CAD or through Using CAD: An Interview Study with Technology Teachers. International journal of technology and design education 33.3: 957–979. [2] Xie, 2018. Learning and Teaching Engineering Design through Modeling and Simulation on a CAD Platform. Computer applications in engineering education 26.4: 824–840. [3] Ye, Peng, Chen & Cai, 2004. Today's students, tomorrow's engineers: an industrial perspective on CAD education, Computer-Aided Design, Volume 36, Issue 14, 2004, 1451-1460.
Keywords: CAD learning