Purdue University awarded $743K NSF grant to transform STEM lab instruction with blended virtual-physical model

Purdue University awarded $743K NSF grant to transform STEM lab instruction with blended virtual-physical model

The three-year project led by Sally Bane along with Jun Chen and ENE's Sean Brophy will up the learning factor in large labs by blending physical and virtual experiments with AI-driven feedback tools. More than 1,500 students will get the opportunity to experiment, design, and problem-solve in new ways.

Purdue University has received a $743,728 award from the National Science Foundation’s (NSF) Division of Undergraduate Education to advance scalable, inquiry-based teaching practices for large laboratory courses in science, technology, engineering, and mathematics (STEM).

The three-year project, Scalable Inquiry-Based STEM Instruction: A Blended Virtual-Physical Lab Concept for Large Lab Courses, aims to address long-standing challenges in delivering self-guided, inquiry-based learning experiences in large lab settings. The project, which runs from September 1, 2025, to August 31, 2028, will be implemented in large fluid mechanics lab courses at Purdue’s West Lafayette and Indianapolis campuses, reaching more than 1,500 students annually.

“Traditional labs give students valuable hands-on practice, while virtual labs allow for flexibility and scale,” said the project’s principal investigator Sally Bane, associate professor in the School of Aeronautics and Astronautics and director of its laboratory and hands-on education. “This project combines the best of both worlds, giving students opportunities to experiment, design, and problem-solve in ways that would be difficult to achieve with only one format.”

Led by Bane with co-investigators Sean Brophy, associate professor of engineering education, and Jun Chen, professor of mechanical engineering, the project introduces a new instructional framework called Scalable Inquiry-Based Lab Experiences (SIBLE). This model blends physical and virtual experiments with AI-driven feedback tools, enabling students to engage in authentic, inquiry-based lab work at scale.

Brophy’s research within the School of Engineering Education focuses on developing adaptive expertise through simulations, analogical reasoning, and model-based learning. His work examines how students comprehend, analyze, troubleshoot, and design complex systems—and how these proficiencies can be cultivated via thoughtfully designed learning environments.

“Integrating AI-driven feedback into this model allows students to learn more independently while still receiving meaningful guidance,” said Brophy. “This project aims to not only improve student outcomes but also make lab instruction more adaptable across different disciplines.”

The research team will explore three central questions:

  1. How does SIBLE enhance students’ critical thinking, problem-solving, and career readiness compared to traditional methods?
  2. How can effective, meaningful feedback mechanisms be integrated into the framework?
  3. What challenges arise when adapting SIBLE to other STEM courses and institutions?

“Scaling up inquiry-based learning has always been a challenge in large STEM labs because of limitations in equipment, space, and staff,” added Chen, who also serves as the School of Mechanical Engineering’s associate head for facilities and operations. “By combining physical experiments with virtual simulations, we’re creating a more accessible, flexible, and effective learning environment.”

This work is supported through NSF’s Improving Undergraduate STEM Education (IUSE): Engaged Student Learning program, which funds innovative approaches to strengthen STEM education for all students.