2025 ASEE Annual Conference & Exposition

Given rapidly increasing sophistication and wide public access to Artificial Intelligence (AI) software, the academic community is struggling with how to best incorporate this new technology into the classroom. The challenge is to use and leverage the capabilities of this new tool to enhance the academic experience without compromising student learning, engineering rigor or academic integrity. This paper describes and assesses the incorporation of AI into an existing computer laboratory course in an undergraduate structural engineering program.

ARCE 352 (Structural Computing I) is a one-unit computer laboratory that is a companion course to ARCE 302 (Structural Analysis) at xx university. The students learn the theory and by-hand methods for finding forces and deflections in indeterminate structures in ARCE 302. In ARCE 352, the students use commercial software and Python programming to solve more complex problems of the same type on a computer.

After receiving classroom instruction on Python and creating the code for several assigned programs, students are required to use Chat GPT or any other AI platform to create Python code for a structural engineering application. As an embedded indicator for ABET Student Outcome 7 (Lifelong Learning), students must learn and experiment with Chat GPT on their own. As support for Student Outcome 3 (Effective communication), students write an essay about their results, their AI experience, the learning strategies they applied, and the effectiveness and limitations of using AI to write computer code. The students then use AI to rewrite their essay and comment on what they learned about the quality of their own writing.

After running this exercise over several iterations of the ARCE 352 course, this paper includes the assessment results, lessons learned, conclusions on the effectiveness and challenges of incorporating AI into an engineering computer course, and suggestions for the future. The assessment comes from student surveys, the student results on the assignments, and the collective judgement of the faculty teaching this course.