Understanding the Impact of Applying Large Language Model in Engineering Design Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In the contemporary era of engineering education, the integration of large language models, offers a novel perspective on enhancing the design process. This study investigates the impact of ChatGPT-3.5 on mechanical engineering design education, focusing on concept generation and detailed modeling. By comparing outcomes from artificial intelligence (AI)-assisted groups to those without AI assistance, our research reveals that AI significantly broadens concept generation diversity but also introduces bias for existing popular designs. Additionally, while AI aids in suggesting functions for computer-aided design (CAD) modeling, its textual nature and the occurrence of unreliable responses limit its usefulness in detailed CAD modeling tasks, highlighting the irreplaceable value of traditional learning materials and hands-on practice. The study concludes that AI should serve as a complement to, rather than a replacement for, traditional design education. Additionally, it highlights the necessity for further specialization within AI to enhance its effectiveness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it