Collaborative Research: Data Science and Digital Twin for Active Learning in Advanced Manufacturing

This project aims to serve the national interest by preparing undergraduate students for careers in advanced manufacturing through the integration of ethical data science and digital twin practices. As the manufacturing industry becomes increasingly data-driven and intelligent, there is a growing need to ensure students not only gain technical expertise but also develop the ethical awareness required to navigate complex real-world challenges involving privacy, security, and responsible innovation. This Level 1 Engaged Student Learning project addresses the importance of ethical decision-making in smart manufacturing systems by creating hands-on, interdisciplinary learning experiences. The project seeks to enhance student competencies, increase workforce readiness, and foster a culture of responsibility among future engineers. The project goals include the development, implementation, and iterative improvement of a comprehensive eight week summer research and training program hosted at the University of Georgia and the University at Buffalo. The program will engage students in applied learning through four key components: theoretical instruction, hands-on modules, professional development, and guided reflection. Students will explore ethical issues across the digital manufacturing lifecycle, while participating in collaborative learning and research activities across both institutions. The project will use a mixed-methods assessment strategy, including pre-, mid-, and post-program evaluations, to measure student growth in ethical reasoning and data science skills. Long-term impacts will be tracked through student career outcomes, with ongoing input from an industry advisory board. 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.