2026 ASEE Annual Conference & Exposition

Artificial intelligence interfaces are set to transform computer-aided design and manufacturing, and more broadly, engineering pedagogy. An example of this trending development in CAM is AI-assisted toolpath planning, which facilitates the configuration of tooling and toolpaths. Engineering students, from various majors, are often tasked with developing CNC toolpaths to fabricate parts. Configuring these codes usually involves algorithmic thinking that integrates spatial visualization, knowledge of industry protocols, and toolpathing. This work-in-progress aims to examine the impact of AI tools on short and long-term goals for engineering students. Does AI assistance improve the speed and accuracy of completing CNC projects? Will frequently relying on AI tools come at the expense of students’ higher-order thinking skills?
A pool of students is recruited with modest prior experience with CNC programming, having taken MAE152, a one-credit practical survey course in manual and CNC machining. The participants are split into two groups, AI-assisted and non-AI-assisted. Each group receives a short training on their assigned workflow. Students are tasked with manufacturing two pre-designed parts using the assigned workflow. They must set up the CNC toolpaths and run the machine to produce the part. The first part is intended to be relatively easy for both groups to accomplish. The second part is designed to be challenging but achievable, with real-world geometry and contextual requirements chosen for the second part that require a more careful choice of strategy. Finally, students are tested for more profound understanding through judging a variety of existing machining setups for errors and inefficiencies: some good, some bad; some with obvious mistakes, others more subtle. Mastery is evaluated in numerous ways. In the manufacturing stage, we track the number of errors, the amount of assistance students need for completion, the suitability of the finished part to requirements, and process efficiency as evidenced by setup and machining time and tool usage. In the evaluation stage, students are scored on their ability to critique the proposed job accurately. Credit is awarded for identifying errors, with the more critical ones being better, and deducted for irrelevant concerns or bad recommendations.
Comparatively evaluating the results of each group’s work (AI-assisted versus human-driven CAM job sequences) provides valuable insights into the impact of AI-assisted CAM Interfaces on engineering students’ learning of CNC Programming Skills. A broader insight from this study also reveals that AI-assisted interfaces will necessitate new learning and teaching methods that incorporate AI tools into engineering curricula, all while promoting students' critical thinking and deep conceptual understanding of STEM topics. Such higher-order thinking skills are crucial for effectively utilizing AI solutions in manufacturing engineering.