2026 ASEE Annual Conference & Exposition

In this work-in-progress paper, we describe initial results from undergraduates’ responses to a survey about their programming/coding experience and use of a custom AI tool within a first-year engineering design course. We investigated students in a cornerstone first-year introduction to engineering design course whose goal is to inspire first-year students to persist in engineering and portray engineering as an achievable option for students. As programming becomes more popular in K-12 settings, introductory classes are seeing a wider range of student coding competencies. This divergence makes it difficult to address the needs of all students in one course. Since Fall 2023, this course has implemented AI tools into its core curriculum. Students build physical artifacts using LEGO Education SPIKE Prime Robotics Kits as they solve individual and group challenges over the course of the semester. They have the option to create code using an AI platform designed specifically for the course, or write their own. The AI platform assists learners by producing beginner-level Python code for Spike Prime. By using this AI, students with little or no prior coding experience can implement complex ideas and access a wide range of engineering practices that would otherwise be limited by their coding competencies.

We explore the following research questions to better understand student practices and the ways the course may or may not be achieving its goals:

- Are there differences between the reported coding habits of students with low and high initial coding experience? Do the differences change over time?
- Are there differences in how students with low and high initial coding experience rank their feelings about their coding experience over the semester?
- Are students’ feelings about their coding experiences and coding habits related?

We found that there were no clear differences between groups’ prior programming experience and students’ rating of their feelings about course coding experience, or between student rating of their feelings about course coding experience and their coding method(s). This result suggests that students from both groups may have similar experiences in these regards despite their difference in initial coding experience, implying that the AI chatbot could be helping the teaching team achieve their goals. When it came to coding preferences, students with high and low prior coding experiences were not stratified in the first half of the semester, but differences emerged between the groups in their final project. We saw significant shifts in the way students choose to code between the mid-semester and end-of-semester surveys, with students moving from using the course AI chatbot’s code without much modification to a wider range of coding methods.

While we are reluctant to make any conclusions from survey data alone, this initial analysis provides insights to explore further in the larger study. Next, we plan to investigate other data sources: open-response survey questions, student coding logs, interviews, student journals, course data, and classroom recordings. With this future work, we seek to explore how the AI chatbot can provide rich robotic engineering design experiences even for first-year students with low prior coding experience.