Educating Generative Designers in Engineering

University Of Texas At Austin, Austin TX

Investigators

Abstract

With support from NSF's Accelerating Discovery program, this project aims to re-envision undergraduate engineering education to include generative design. Generative design is a transformative design technology that uses open-ended artificial intelligence algorithms to arrive at solutions for engineering problems. Generative design software can be freed from preconceived ideas or past solutions. As a result, it allows exploration of a wider variety of potential solutions, with the goal of arriving at an optimal solution in partnership with the human engineer. The proposed project will support the development of open-source educational tools for teaching and learning generative design. These tools will be based on existing computer-assisted design and engineering software, and will include a set of project modules to guide students through authentic design problems. The software and associated design problems will be pilot tested by students at thirteen institutions, including community colleges, Historically Black Colleges and Universities, liberal arts colleges, and public universities. Information from these pilots will be used iteratively to refine the software and teaching approach. This project represents a novel application of artificial intelligence to engineering that could augment the creativity and productivity of the engineering workforce of the future. The overall goal of this project is to facilitate the teaching and learning of generative design at the undergraduate level. To accomplish this goal, the University of Arkansas, the University of Illinois at Urbana-Champaign, Oregon State University, and the Concord Consortium will collaborate to define, implement, and disseminate generative design tools and projects for use in undergraduate courses. Research questions from three perspectives will drive the project: 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? and 3) Affective perspective: To what extent and in what ways can artificial intelligence affect the professional formation of engineers as indicated by the changes of students' interest and self-efficacy in engineering? To answer these questions, interdisciplinary research that integrates the perspectives and knowledge in engineering design, computer science, learning science, and workforce development will be conducted. The project will involve more than 1,000 students at 13 institutions around the country. The research will include data from demographic surveys, questionnaires, self-efficacy measures, design reports, screencast videos, classroom observations, and participant interviews. The materials developed by the project will be open source, including an open-source tool for teaching and learning generative design and a set of project-based learning modules that guide use of the tool to solve authentic design problems in architectural engineering and energy engineering. The products of this project are expected to equip students with essential skills and mindsets needed to master using artificial intelligence approaches in contemporary engineering practices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.