Investigating How Problem-Based Projects Can Shape Engineering Students' Use of Generative AI for Coding
TL;DR
This research investigates whether this programming challenge would encourage students to adopt programming-specific generative AI strategies, e.g. code editor embedded AI assistants, when presented with the option, and indicates that students would benefit from structured training in effective generative AI usage for software coding.
Abstract
Generative AI is rapidly transforming workflows in software development. In mechanical engineering education, software skills are taught as a supplemental tool for modeling, but students often lack the experience to leverage AI assistance to approach larger-scale, complex coding tasks. Following a problem-based learning design, 230 third-year students were given a cart-pole system and a series of milestones to achieve. With their choice of generative AI support, they wrote C and Python code to control and analyze data from the cart-pole. We investigated whether this programming challenge would encourage students to adopt programming-specific generative AI strategies, e.g. code editor embedded AI assistants, when presented with the option. Data collected from TAs and student questionnaires indicated that most students continued with default approaches of using chatbots for code generation and debugging. These results indicates that students would benefit from structured training in effective generative AI usage for software coding.