Assessing the Prevalence of Artificial Intelligence in Mechanical Engineering and Design Curricula
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".