Collaborative Research: Bifocal Reasoning Integrating Digital Twins to Guide Engineering Education

This project aims to serve the national interest by improving curricula in undergraduate engineering education through the integration of digital twin technologies into existing engineering courses. Digital twins, real-time virtual representations of physical systems, are increasingly central to modern industry and workforce preparation, underscoring the importance of creating structured and scalable learning experiences that help students reason about complex engineering systems. This project plans to design and implement accessible digital twin-based learning experiences that enable students to actively explore, compare, and reason about physical and virtual system behavior. The project is positioned to contribute evidence-based practices with clear significance that strengthen students’ engagement, conceptual understanding, and preparation for emerging engineering careers. By integrating digital twin technologies with theory-driven instructional design, this Level 2 Engaged Student Learning project seeks to advance educational practice and generate new insights at the intersection of engineering education, learning sciences, and cyber-physical systems. The goals and scope of this project are to design, develop, and study a digital twin-enabled instructional framework, learning modules, and cyberinfrastructure that support undergraduate engineering students’ bifocal reasoning, defined as the ability to coordinate physical system behavior with computational models. The project intends to leverage the Artificial Intelligence (AI) Hardware Board, a low-cost and modular digital twin platform developed by the project team to provide scalable infrastructure for implementing bifocal modeling in engineering settings. Guided by learning sciences theories and prior work in bifocal modeling and learning-by-doing, the project intends to investigate how integrated digital twin learning experiences influence students’ systems thinking, conceptual understanding, and STEM identity. Using mixed-methods design-based research and quasi-experimental approaches, the project seeks to examine its effectiveness across studies involving multiple instructors and institutions. The project includes analyses of student learning outcomes, engagement measures, and implementation fidelity for assessment and evaluation. The project plans to disseminate project results, instructional materials, and software tools through open-sourced platforms, peer-reviewed publications, and professional conferences to support continued research and adoption by the broader engineering education community. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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.