Principal Investigator: Mark Riedl
CoPrincipal Investigator(s): Jeff Rosen, Meltem Alemdar, Roxanne Moore
Organization: Georgia Tech Research Corporation
NSF Award Information: Exploring Artificial Intelligence-enhanced Electronic Design Process Logs: Empowering High School Engineering Teachers
Abstract:
The Engineering Design Process (EDP) is a general theoretical framework often used for teaching engineering, STEM, invention, and even science, particularly in K-12 education. While most EDPs used in education are depicted as linear or circular, true design processes are highly creative, non-linear, and often involve ill-posed problem statements and solution criteria. These traits make it particularly difficult for high school engineering teachers, who tend to skip over key elements of human-centered design, where an engineer takes time to understand the problem through research, interviews, prior literature searches, market analysis, and brainstorming—the steps where diversity of thought and experience are of the most value. In addition, it can be hard to provide students with real-time feedback due to the asynchronous nature of group work and large class sizes, and students may not feel comfortable asking for feedback on incomplete work. This project will develop and pilot an artificial intelligence (AI) enhanced Engineering Design Process Log to help students navigate the design process, provide real-time feedback, and encourage meaningful documentation of each step of the process. This project does not propose to replace teachers with AI; rather, the project will explore a novel approach in which AI systems assist teachers in the creation of instructional modules that adhere to EDP best practices. This project is a collaboration between researchers at Georgia Tech’s College of Computing (GT CoC) and researchers at Georgia Tech’s Center for Education Integrating Science, Mathematics and Computing (CEISMC).

This project is a teaching-focused technological innovation, representing an early exploration into AI-enhanced design pedagogy. Specifically, the project will: 1) Improve upon an existing web-based Engineering Design Process Log (EDPL) by engaging in teacher user studies, 2) Design, pilot, and implement an AI-based authoring and tutoring system for teachers to customize feedback for students and for specific projects with domain expertise, 3) Design and provide professional development opportunities for alpha and beta testing teachers, and 4) Assess the impact of an AI-based EDP Log (AI-EDPL) on engineering design pedagogy and classroom practice. The AI-EDPL software system will use concepts initially pioneered for intelligent tutoring systems, but applied to scaffolding the creation of custom, teacher-made instructional materials that adhere to best practices in design process pedagogy assessment. Unlike many other educational domains, engineering design problems vary widely in scope and solution pathways, which means there will not be a one-size-fits-all tutoring system that can provide feedback to students. This project will examine (a) whether artificial intelligence can support and scaffold teachers in the creation of the necessary models and knowledge structures needed to scaffold and support learners, and, (b) what professional development teachers need to be successful in developing these models. A multi-phased approach will be used, using value-sensitive design processes from the field of human computer interaction to develop minimalist functional systems that can be tested with teachers in classrooms. In order for AI to help teachers, who do not have a lot of time to tinker with software, they must be able to express their intentions in natural language, which must be automatically converted into functional approximations of the task models that can be easily edited. This project will build on best practices in design theory and pedagogy, design documentation, design instruction, design assessment, and AI tutoring to create a one-of-a-kind technology suitable for engineering design instruction at the high school level. It represents a first attempt at providing real-time feedback in a computational setting for an open-ended design challenge, and it does so without marginalizing or diminishing the role of the instructor in the engineering classroom.

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.