Generative artificial intelligence models outperform students on divergent and convergent thinking assessments
Bibliographic record
Abstract
Generative artificial intelligence (GenAI) has garnered significant attention for its remarkable capabilities and is now widely used in many creative domains, sparking scientific interest in its creative capacities. While previous studies have assessed GenAI on creativity tasks, there is no evidence comparing the latest GenAI models to the same sample of humans on both divergent and convergent thinking assessments. In this study, we compared the creative ability of human participants (n = 46) against three state-of-the-art GenAI chatbots-ChatGPT-4o, DeepSeek-V3, and Gemini 2.0-using the Alternate Uses Task (AUT) and the Remote Associates Test (RAT). For divergent thinking, we compared the median and maximum originality scores on the AUT, representing the level of originality of the 'average' and 'best' idea. We then compared performance on 57 RAT items as a measure of convergent thinking. All GenAI models outperformed human participants in both tasks: the 'average' and 'best' GenAI ideas were significantly more original than human-generated ideas. All GenAI models demonstrated superior RAT performance vs. humans. Among GenAI models, ChatGPT-4o consistently demonstrated the best scores on both tasks. These findings illustrate the immense creative potential of GenAI, but call into question the appropriateness of current creativity assessment methods in the study of GenAI creativity.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".