Evidence map›Paper›PMID 41125797›Full record

ArticleScientific reports2025

Generative artificial intelligence models outperform students on divergent and convergent thinking assessments.

Vikram Arora, Alex Thabane, Sameer Parpia, Goran Calic, Mohit Bhandari

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Vikram AroraDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada. v.arora@mail.utoronto.ca.
Alex ThabaneDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Sameer ParpiaDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Goran CalicDeGroote School of Business, McMaster University, Hamilton, ON, Canada.
Mohit BhandariDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceCreativityStudentsThinkingAdultFemaleGenerative Artificial IntelligenceHumansMaleYoung AdultArtificial intelligenceConvergent thinkingCreativityDivergent thinkingLarge language model

Identifiers

PMID41125797
PMCPMC12546817

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.