Understanding the Differences in an AI-Based Creativity Support Tool Between Creativity Types in Fashion Design
Bibliographic record
Abstract
As the perspective on creativity shifts to “how it is expressed,” research has aimed to categorize it by problem-solving style. Since harnessing individual creative traits can positively impact creative performance, there has been an emphasis on designing computer systems that are tailored to personal problem-solving behaviors. AI-CST has opened up the potential to facilitate such customization. In this work, we consider two types of creativity—adaptors and innovators—based on problem-solving styles, and investigate AI-CST designs that both types could flexibly use according to the fashion design process. We identified two main AI-CST functions—determining design direction and receiving design inspiration—of the fashion design process, and developed CoCoStyle to map these functions. Through a user study with 30 fashion professionals (15 adaptors and 15 innovators), we found significant differences between the two groups from survey responses, system usage logs, and interviews. Based on the results, we discuss the theoretical and practical implications of AI and AI-CST where creativity is essential.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".