The Personas of Cloud CAD Collaboration: A Case Study of a Team of CAD Professionals
Bibliographic record
Abstract
Computer-aided design (CAD) has become a fundamental tool in engineering projects, particularly in product design and development. Recent advancements have shifted CAD systems to the cloud, referred to by us and others asCloud CAD, offering a new realm for collaboration in product development projects. The transition to Cloud CAD introduces substantial changes to how one might manage product design teams, impacting how design tasks are divided among team members, the choices designers make in undertaking different tasks, and the additional responsibilities team members must fulfil. In this paper, we investigate the “personas,” described as patterns of activity representing an engineer's roles and responsibilities, that are essential to successful collaboration in Cloud CAD. To achieve this, we conducted a mixed-methods case study of a self-organized, time-bounded, and geographically distributed team of CAD professionals. This unique setting allowed us to identify and understand the personas that engineers adopt during Cloud CAD projects, where the engineers are not constrained to predefined roles and responsibilities. By analyzing CAD user action logs, the final CAD model, and semi-structured interview transcripts, we identified three integral personas in Cloud CAD projects: the guide, the integrator, and the communicator. We further observed that the emergence of each persona is temporally dependent, varying at different stages of the design process. Our work contributes an in-depth analysis of three personas in Cloud CAD, their relevance and benefits to CAD projects, and practical implications for engineering managers to support effective Cloud CAD collaboration.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".