Session: DEC-01-01: Design Across the Curriculum

Paper Number: 143693

143693 - Assessing the Prevalence of Artificial Intelligence in Mechanical Engineering and Design Curricula 

Engineering curricula undergo frequent change, driven by new technologies and industry needs. Today we are witnessing a significant rise in artificial intelligence (AI) tools, with applications not only across engineering, but specifically in critical endeavors such as design. Given the interest in students in AI techniques, the demand of engineering design employers to hire students with such knowledge, and the fast-evolving nature of the AI field, compared to the slower pace of curriculum evolution, there is thus a need to assess curricular content related to AI in the mechanical engineering curriculum. The purpose of this paper is to provide a baseline assessment of the current prevalence of AI-driven methods and approaches in engineering design education. Current approaches for curricular assessment tend to be resource-intensive and narrow in scope, limiting our capability for large-scale and timely data analysis. Thus we develop a using a novel approach for curriculum data collection and assessment: First, we use web-scraping to collect the titles and descriptions of 2,195 courses in 28 undergraduate mechanical engineering programs. Next we use a list of relevant keywords to search for AI topics in these courses. We find 32 AI-focused courses available to mechanical engineering students, in which nine courses integrate AI and engineering design. These results indicate the limited but emerging prevalence of AI-based courses in engineering design education.

Presenting Author: Pranav Milind Khanolkar University of Toronto

Presenting Author Biography: Pranav Milind Khanolkar is a Ph.D. candidate at Ready Lab at University of Toronto. He holds a Master’s degree in Industrial Engineering from The Pennsylvania State University and Bachelor’s degree in Mechanical Engineering from University of Mumbai. At Penn State, he worked as a Graduate Research Assistant in THRED Group, with his research focused towards application of deep learning for structural analyses. His research interests involve applications of AI algorithms for automating product design processes and studying designer-automation interaction.

Authors:

Pranav Milind Khanolkar University of Toronto
Jerry Lu University of Waterloo
Ada Hurst University of Waterloo
Alison Olechowski University of Toronto