Artificial Intelligence Enhances Human Creativity Through Real-Time Evaluative Feedback
Bibliographic record
Abstract
The integration of artificial intelligence (AI) into creative work continues to expand, yet its impact on human creativity itself—beyond simply providing ideas—remains uncertain. We reposition AI’s role from idea generator to idea evaluator, using trained models to provide real-time feedback on human-generated ideas. Across two studies—a preregistered online experiment involving individuals with varying levels of expertise (N = 554) and a large-scale naturalistic experiment during a year-long museum exhibit (N = 36,198)—participants generated solutions to real-world problems or created visual sketches. AI feedback significantly improved participant originality in both verbal and visual creative domains. Mediation analyses revealed these gains were partly driven by changes in individuals’ self-evaluation of their own originality, implying a key role for metacognition—the ability to monitor, control and regulate one’s thinking. These findings suggest that AI’s potential extends beyond generation to include idea evaluation, helping humans assess and refine their ideas through real-time feedback.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".