Reframing engineering design: integrating design thinking and systems thinking in engineering education and practice to address wicked problems
Bibliographic record
Abstract
The conceptualization of “engineering design”, as outlined by the Canadian Engineering Accreditation Board (CEAB), has shifted over the years, however, the gap in engineering education remains a prevailing deficiency in engineering education and practice lies in the exclusion of non-technical competencies (such as empathy, communication, innovation, and creativity) that are impeding engineers from effectively addressing complex issues. These frameworks offer a robust methodology for tackling complex, dynamic, and interconnected challenges—referred to as “wicked problems”. In addition, this paper proposes a fourth-year engineering design course that explicitly incorporates these approaches, addressing the identified gap in social competencies within engineering education. By integrating these approaches into the foundation of engineering design education, there may be an avenue to equip engineers with the skills needed to empathize with stakeholders, understand contextual landscapes and generate meaningful solutions that contribute positively to society.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".