Collaborative Research: EAGER: Agency and Learning through Industry-informed GenAI Navigation

About This Grant

From designing processes to writing reports and modeling complex systems, Artificial Intelligence (AI), especially generative AI (GenAI), is transforming how engineering work is done, yet engineering students are often unprepared for this new reality. Additionally, because much of the technology used in industry is proprietary, protected by intellectual property laws, or cost-prohibitive, educators often lack access to up-to-date information about how AI is actually being used in the field. This limits their ability to teach students the ethical, technical, and professional skills they need to succeed in AI-integrated workplaces. This project will respond directly to this challenge by creating new ways for faculty and students in chemical engineering to learn how AI is used in the workforce and how to use it responsibly. The project supports NSF’s goals to strengthen the U.S. STEM workforce, promote economic competitiveness, and ensure that emerging technologies like AI benefit all Americans. By creating tools that help bridge the gap between industry practices and classroom instruction, this project will help prepare students for high-tech careers while reinforcing responsible innovation and excellence in engineering education. This project will investigate how GenAI is used across different levels of chemical engineering practice and explore how students, instructors, and professionals perceive and interact with GenAI tools. The research team will conduct interviews and focus groups with chemical engineering professionals, faculty, and students to understand their beliefs and practices related to GenAI. Using discourse analysis and qualitative analysis, the team will examine how individuals display agency over, share agency with, attribute agency to, or offload agency onto GenAI tools. These findings will inform the development of a novel survey instrument designed to help faculty stay up to date with evolving professional GenAI practices while considering workplace privacy and proprietary constraints. The results will support timely curricular updates, enabling faculty to embed relevant and ethical GenAI instruction into engineering education. The project will generate guidance for curriculum developers, offer insights into workforce readiness, and advance research on human-AI agency. The research will be conducted by universities in states that serve a high proportion of first-generation and veteran students, expanding educational opportunities for those students and aligning with NSF’s commitment to broadening participation without exclusion. Ultimately, this work will lay the foundation for a scalable, research-based approach to keeping engineering education aligned with the fast-evolving landscape of AI-integrated professional practice. 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.