Exploring Enhancement of Student Learning Outcomes by Redefining Problem Generation in Mechanical Engineering Using Generative AI

Abstract Engineering problems have long been integral to classroom instruction, serving as essential tools for educational development. However, the generation of these problems has remained constant over the years. The advent of Generative Artificial Intelligence (Gen-AI) offers a new opportunity to enhance problem generation in engineering. In this paper, we introduce an innovative approach that leverages the unique capabilities of Gen-AI to redefine how mechanical engineering problems are generated. Using a mixed-method research design, we explore students’ performance, preferences, mental workload, and emotional responses across various problem sources from the manufacturing domain in mechanical engineering, specifically traditional textbook-based problems and Gen-AI-generated problems. The findings of this research reveal significant impacts of Gen-AI-generated problems on student output towards the problems. Preliminary insights from this research contribute to advancing engineering pedagogy by demonstrating Gen-AI’s potential to transform traditional problem generation methods and enrich students’ learning experiences.