Evaluating Computer-Aided Design Software as a Barrier to Women’s Engagement in Engineering: A Focused Literature Review
Bibliographic record
Abstract
Abstract To tackle today's toughest problems, like climate change and the threat of global pandemics, design teams will need to deliver not only software solutions, but also innovative hardware products, sometimes called "tough-tech" or "hard-tech." Computer-Aided Design (CAD) is a key tool for these design teams to leverage in order to reach creative, innovative, hard-tech solutions for society's most pressing issues. Given CAD's importance in design, it is positioned to be a key enabler, or barrier, to increasing diversity in design teams. Statistics on the representation of women in CAD-reliant engineering fields, such as mechanical engineering, show that the numbers remain much below the overall female representation in engineering, and far below gender parity. Differences in confidence and ability level with CAD software are factors that may explain why this disparity exists, and so a focus on increasing accessibility of this tool provides an opportunity to increase female and non-binary representation in design teams. In this paper, we conduct a focused literature review to provide a comprehensive understanding of what factors position CAD as a barrier to women's engagement in engineering. The primary finding of this literature review is, in fact, the lack of literature; a deep body of knowledge exists to understand Women in Engineering and gender barriers in the profession more broadly, and in parallel, a rich literature on considerations for designing effective and efficient CAD tools and training exists. Yet we see a distinct lack of literature with a primary focus on the intersection of gender considerations in CAD. Based on the limited literature we did discover, we identify several potential barriers to gender diversity in CAD-reliant engineering fields: gender bias in CAD training, lack of representation of women in CAD communities, gender disparity in spatial reasoning skills, and differences in self-efficacy levels. With knowledge of these barriers, we then propose two strategic approaches for leveraging CAD as an avenue to increase gender diversity in mechanical design, which incorporate course design, outreach activities, and general considerations for engineering. These preliminary recommendations can be utilized by educators to support women and non-binary students towards the goal of creating more diverse design teams in undergraduate studies and beyond, ultimately leading to the innovative and creative hard-tech solutions needed to solve society's biggest problems. Importantly, we aim for this paper to act as a motivator to conduct further research in the area of gender considerations in CAD tools and trainings to better understand the actual barriers that women and non-binary individuals face in this field.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".