Author(s):
Dr. Zhenghui Sha (PI)1, Dr. Molly Goldstein (Co-PI)2, Dr. Onan Demirel (Co-PI)3, Dr. Charles Xie (Co-PI)4, Dr. Darya L. Zabelina (Co-PI)5, John Clay1, Xingang Li1, Elisa Koolman1
1 University of Texas at Austin, 2 University of Illinois at Urbana-Champaign, 3 Oregon State University, 4 Institute for Future Intelligence, 5 University of Arkansas
Need: Design thinking research investigates how expert designers use the cognitive process which underlie design to understand how students can be effectively taught informed design practices. However, recent advancements in engineering design technology have changed how designers work and thus requires a reconsideration and extension of design thinking research. Generative design (GD) is a computational design technology that transforms the design process by introducing open-ended artificial intelligence (AI) algorithms to create solutions to engineering design problems. GD augments the human-driven traditional design process and allows the designer to explore a wider range of solutions compared to human cognition. The adoption of generative design products/features introduced by leading CAD companies (e.g., PTC and Autodesk) may suggest a new era of engineering is emerging. Engineering education and research must be updated to reflect the paradigm shift from traditional to generative design, which fundamentally changes the role of the human designer and the required design thinking.
Guiding Questions: This project is motivated by three Research Questions from three unique perspectives: 1) Theoretical perspective: What are the essential elements of generative design thinking that students must acquire so they can work effectively at the human-technology frontier in engineering? 2) Practical perspective: To what extent and in what ways can the curriculum and materials support the learning of generative design as indicated by students’ gains in generative design thinking? 3) Affective perspective: To what extent and in what ways can AI affect the professional formation of engineers as indicated by the changes in students’ interest and self-efficacy in engineering?
Outcomes: We are conducting interdisciplinary research which integrates the perspectives and knowledge of engineering design, computer science, learning science, and workforce development to answer these questions. Major outcomes project outcomes include: 1) the design and development of Aladdin, an open-source CAD software with generative design functions, 2), a systematic review of deep learning of cross-modal task (DLCMT) methods, 3) research to develop an operational definition of generative design thinking (GDT); 4) the development of project-based generative design to support engineering education; and, 5) the development of a curriculum documents and practice activities to teach traditional, parametric, and generative design concepts.
Broader Impacts: Project outcomes will investigate and prepare materials to teach students the skills and approaches necessary to master AI in the modern engineering design landscape. Open-source educational materials for the project will include project-based instructional modules, curriculum text, and practice activities for teaching, learning, and applying generative design concepts to solve real-world problems in product design, architectural engineering, and energy systems design. Around 700 students have completed the introductory generative design module throughout the project. Next steps will involve over 1,000 students at 13 institutions around the country. These impacts in engineering education and research will contribute to the Future of Work at the Human-Technology Frontier, one of NSF’s 10 Big Ideas. Additionally, a generative design research and education workshop will be hosted to further disseminate project impacts and inform next steps.
Coauthors
Zhenghui Sha, University of Texas at Austin; Molly Goldstein, University of Illinois at Urbana-Champaign; Onan Demirel, Oregon State University; Charles Xie, Institute for Future Intelligence; Darya Zabelina, University of Arkansas; Xingang Li, University of Texas at Austin; Elisa Koolman, University of Texas at Austin
