Using AI to Generate Visual Art: Do Individual Differences in Creativity Predict AI-Assisted Art Quality?
Bibliographic record
Abstract
As artificial intelligence (AI) advances in the realm of generative art, a critical question emerges: does human creativity matter? That is, do more-creative people produce more-creative AI-assisted artwork? To explore this, we conducted an online, pre-registered study in which we measured individual differences in creativity through two divergent-thinking tasks: The Alternate Uses Task and the Divergent Associations Task. Separately, participants produced creative wordsets for a hypothetical AI-art generator, which we then input into DALL-E to generate images. A group of trained raters independently assessed these images for creativity. Results revealed that both DAT performance and semantic diversity of the wordsets positively associated with the creativity of the AI-assisted images, suggesting that individuals with stronger divergent-thinking skills, and those who generated more-creative wordsets, tended to inspire more-creative AI-assisted artwork. Mediation analyses supported this conclusion by demonstrating a significant pathway between individual creative ability and AI-art creativity, mediated by semantic diversity. However, while our models yielded significant results, the effect sizes were modest, suggesting that the relationship between individual creative ability and AI-assisted creative outputs is relatively small. Taken together, these results suggest that while individual creativity appears to contribute to the quality of AI-assisted artwork, its influence may be relatively limited.
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".