Digital twin technology for engineering education: An experiential learning case study

Abstract

The evolving landscape of modern manufacturing demands a workforce equipped with both theoretical knowledge and practical hands-on skills. This paper explores experiential engineering education through a case study of undergraduate interns integrated within a small manufacturer to extend classroom learning through Digital Twin (DT) technologies and Internet of Things (IoT) data collection. Interns participated in data collection, system modeling, and decision-making tasks, enabling comparison between traditional and smart manufacturing environments. Preliminary results indicate that exposure to DT and IoT frameworks improved students’ understanding of automation hierarchies, strengthened systems thinking, and enhanced data-driven problem-solving skills. The study evaluated intern performance and identified benefits for both students and employers. While limitations related to scope and assessment are acknowledged, the initial findings suggest that DT–enabled internships provide a valuable pathway for aligning engineering curricula with industry needs and preparing graduates for the demands of modern manufacturing environments.