Enhancing Curriculum Pathways and STEM Education with Emerging Artificial Intelligence Technologies for Innovations in Smart Manufacturing

This project aims to serve the national interest by investigating how artificial intelligence (AI), digital twins, robotics, and simulation-driven manufacturing technologies can be effectively integrated into engineering technology education to improve student learning and workforce readiness in smart manufacturing. The United States manufacturing sector is undergoing rapid transformation driven by advances in AI, robotics, automation, and digital technologies. However, manufacturers continue to report significant workforce shortages in areas requiring expertise in smart manufacturing, robotics, data analytics, and digital engineering. This project plans to enhance undergraduate STEM education by developing and implementing innovative curriculum pathways, laboratory experiences, and outreach activities focused on AI-enabled smart manufacturing. Students will engage in hands-on and simulation-based learning experiences involving robotics, digital twins, intelligent automation, computer vision, and data analytics. The project also plans to increase participation in STEM through mentoring, K–12 outreach, teacher engagement, and experiential learning activities that connect students with emerging manufacturing technologies and career opportunities. By preparing a technologically skilled workforce, the project addresses a critical national need for talent capable of supporting U.S. manufacturing competitiveness, innovation, economic growth, and technological leadership. The project is designed to develop, evaluate, and disseminate of educational resources that can be broadly adopted to strengthen workforce preparation across the nation. The project aims to develop and assess instructional modules, laboratory activities, and project-based learning experiences focused on digital twin development, virtual commissioning, industrial and collaborative robotics, Industrial Internet of Things (IIoT), machine learning, computer vision, predictive maintenance, and simulation-informed manufacturing. Students will utilize advanced digital engineering platforms to model, simulate, validate, and optimize manufacturing systems using data-driven approaches prior to physical implementation. The project is designed to examine how simulation-based learning, AI-enabled decision support, and human-machine collaboration influence student skill development, systems thinking, persistence, and engagement in STEM. A mixed-methods assessment framework will be used to evaluate learning gains, workforce competencies, and career readiness while generating evidence-based knowledge regarding effective educational practices for AI-enabled manufacturing. All curriculum modules, laboratory exercises, assessment instruments, and implementation guides will be developed as transferable resources that support adoption by community colleges, and universities with varying levels of laboratory infrastructure. The project team seeks to contribute scalable models for integrating AI-enabled smart manufacturing into undergraduate education and to advance national efforts to prepare the next-generation workforce for increasingly digital and autonomous manufacturing environments. 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.