GenAI for All Engineers: Infusion of Curriculum on Generative AI into Engineering-wide Undergraduate Education

This project aims to serve the national interest by broadening access to generative artificial intelligence (GenAI) education across the field of engineering. GenAI technologies, exemplified by tools such as ChatGPT and DALL-E, are transforming engineering processes by improving efficiency, innovation, sustainability, and safety across areas such as product design, smart manufacturing, drug discovery, robotics, and software development. However, educational programs on GenAI remain largely confined to computational sciences and mathematics, leaving a critical gap in preparing a broader, multidisciplinary engineering workforce for emerging and rapidly evolving AI-powered STEM fields. To address this gap, this Engaged Student Learning Level 2 project plans to develop and offer an educational program called "GenAI for All Engineers" (GenEng) that will provide undergraduate engineering students across multiple disciplines with the knowledge and skills needed to apply GenAI in their respective fields. The project will mobilize a college-wide effort to infuse customized, multidisciplinary curriculum modules on GenAI into courses spanning aerospace, biomedical, chemical and environmental, computer science, electrical and computer, and mechanical and materials engineering departments. The project also plans to establish a dedicated “ML for All Engineers” Research and Education (MLE-RE) laboratory to support hands-on learning and collaborative research on machine learning (ML) and GenAI. By equipping a diverse population of engineering students with GenAI literacy and competency, the project is expected to significantly increase student engagement, broaden participation in STEM, and strengthen pathways into AI-powered engineering careers. The project's central goal is to design and implement the GenEng program to impart GenAI knowledge and skills across the field of engineering. To enable students from all engineering backgrounds to effectively learn GenAI concepts, curriculum of the GenEng program will be strategically designed at both macros and micro levels by building on various evidence-based pedagogical approaches. At the macros level, infused curriculum modules will exhibit a spiral curriculum structure having vertical-horizontal integrations, with the vertically infused modules enabling students to gradually digest increasingly complex GenAI concepts, and the horizontally infused modules providing them with a holistic understanding of GenAI. At the micro level, individual curriculum modules will embed threshold concepts (TCs) within a problem-based learning (PBL) framework constructed using model-eliciting activities (MEAs), thereby fostering engaging, human-machine interactive learning experiences. The project will investigate how TCs in GenAI can be identified within diverse engineering disciplinary contexts and embedded within MEAs to promote transformative learning and will examine how PBL modules can be designed and spirally structured to support students in developing integrative, multidisciplinary competencies. The project plans to rigorously evaluate its impact on aspects such as student learning, recruitment and retention, workforce readiness, and multidisciplinary collaboration, revealing key insights into strategies for empowering students to adapt to emerging and rapidly changing AI-powered STEM fields, developing high-impact GenAI-centered education-to-employment pathways in engineering, and fostering effective multidisciplinary collaborations centered on GenAI. Results, curriculum modules, and best practices generated by the project will be disseminated through diverse channels to enhance public understanding of emerging technological traits, capabilities, and the role of AI/ML in shaping them. 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.