A PILOT STUDY ON THE PREVALENCE OF ARTIFICIAL INTELLIGENCE IN CANADIAN ENGINEERING DESIGN CURRICULA
Bibliographic record
Abstract
Recent advances in artificial intelligence (AI) have shed light on the potential uses and applications of AI tools in engineering design. However, the aspiration of a fully automated engineering design process still seems out of reach of AI’s current capabilities, and therefore, the need for human expertise and cognitive skills persists. Nonetheless, a collaborative design process that emphasizes and uses the strengths of both AI and human engineers is an appealing direction for AI in design. Touncover the current applications of AI, the authors review literature pertaining to AI applications in design research and engineering practice. This highlights the importance of integrating AI education into engineering design curricula in post-secondary institutions. Next, a pilot studyassessment of undergraduate mechanical engineering course descriptions at the University of Waterloo and University of Toronto reveals that only one out of a total of 153 courses provides both AI and design-related knowledge together in a course. This result identifies possible gaps in Canadian engineering curricula and potential deficiencies in the skills of graduating Canadianengineers.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".