SKETCHING AS A COMMUNICATION TOOL FOR SHARED UNDERSTANDING IN CONCEPTUAL DESIGN PHASE OF AUTOMOTIVE DESIGN
Bibliographic record
Abstract
Effective communication is essential for fostering shared understanding during the conceptual phase of automotive design, where ambiguity and rapid ideation are common. This study investigates how sketching functions as an effective communication tool in two different interaction modalities: silent (experimental group) and verbal (control group), to support team alignment and idea development. Eighteen Malaysian automotive designers were assigned to three-person teams and completed a two-stage task involving individual ideation followed by collaborative refinement. Using visual link analysis, the study evaluated design moves, refinement patterns, and the balance of contributions across both conditions. Silent groups exhibited more structured individual sketching behavior and greater refinement during the ideation stage, while verbal groups utilized spoken dialogue during collaboration to negotiate and align concepts. Although statistical differences were not significant, consistent behavioral trends emerged across conditions. These findings suggest that sketching operates as a flexible and self-sufficient communication medium, effectively bridging cognitive gaps regardless of verbal interaction. The study provides empirical evidence supporting sketching’s dual role as both an individual cognitive tool and a shared visual language in team-based design.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".