GenAI Integration into Engineering Education: A Case Study of an Introductory Undergraduate Engineering Course

A research team led by Kadir Kozan published a groundbreaking case study (arXiv:2603.13269) examining the real-world integration of generative AI into an introductory undergraduate engineering course. The study followed one instructor and seven teaching assistants as they implemented GenAI tools for the first time, tracking student performance through four interviews across the semester and analyzing formative exercise results. The findings revealed that after GenAI integration, students' performance on coding exercises showed measurable improvement, though the technology's use declined as the semester progressed.

Interestingly, students initially found the ability to use GenAI in coursework "exciting and surprising," but this enthusiasm waned over time. The integration primarily functioned at a substitution level—replacing existing instructional methods or increasing efficiency in solving coding problems—rather than fundamentally transforming the educational approach. The research team concluded that while GenAI shows clear potential to enhance learning, its implementation requires careful consideration of timing and pedagogical integration to sustain engagement and move beyond mere efficiency gains.