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Record W4415653859 · doi:10.35631/ijirev.722043

SKETCHING AS A COMMUNICATION TOOL FOR SHARED UNDERSTANDING IN CONCEPTUAL DESIGN PHASE OF AUTOMOTIVE DESIGN

2025· article· en· W4415653859 on OpenAlexaff
Mohamad Fairuz Abdul Rahim, Nik Shahman Nik Ahmad Ariff

Bibliographic record

VenueInternational Journal of Innovation and Industrial Revolution · 2025
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersMinistry of Higher Education, Malaysia
KeywordsAmbiguityCognitionIdeationBridging (networking)Empirical researchAutomotive industryConceptual designNegotiationTask (project management)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.205
GPT teacher head0.390
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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