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AI can outperform the average person on some narrow creativity tests, but that does not mean it is more creative than humans overall. A January 2026 Scientific Reports study found that some language models exceeded the human average on a divergent-thinking task, while the most creative half of human participants outperformed every tested model. Results vary by task and study design, and the tests do not establish human-like creativity.
For painters and other visual creators, the practical takeaway is measured: generative AI can help explore variations and visual prompts, but benchmark scores do not measure artistic purpose, judgment, context, revision, or meaning.
The headline result is a split decision
The study, published in Scientific Reports on January 21, 2026, compared several large language models with data from 100,000 human participants. Its results are best understood as a comparison of score distributions, not a single contest between “AI” and “humans.”
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| Comparison | Reported result |
|---|---|
| GPT-4 and average human performance on the DAT | GPT-4 scored above the human average in the tested configuration. |
| Gemini Pro and human performance | Statistically indistinguishable in the reported comparison. |
| Most creative half of humans and tested models | The human group’s average exceeded every tested model. |
| Top 10% of humans and tested models | The top human participants performed better still. |
So “AI beat humans” is too broad. The accurate version is: some AI systems beat average human performance on one constrained measure of divergent creativity, while highly creative humans remained ahead. The tested models were not intended as a definitive ranking of the best systems available in 2026. Results depend on the exact model version, prompt, sampling settings, and test protocol.
What the creativity test actually measured
The main benchmark was the Divergent Association Task (DAT). Participants are asked to produce 10 words that are as semantically different from one another as possible. An algorithm estimates the distance between the words.
A low-divergence list might be “dog, cat, horse, animal.” A high-divergence list could include “galaxy, fork, freedom, algae, harmonica, quantum, nostalgia, velvet, hurricane, photosynthesis.” The second list moves across more distant areas of meaning.
This is one way to measure idea generation, but semantic distance is not the same as artistic quality. The task does not determine whether an idea is useful, emotionally powerful, culturally important, technically feasible, or worth developing.
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Four different meanings of creativity
- Divergent creativity: Generating varied, unusual, or distant possibilities.
- Convergent creativity: Finding one effective solution to a specific problem.
- Creative achievement: Producing work that matters to audiences, communities, or culture.
- Creative labor: Defining a purpose, understanding an audience, making choices, revising, and taking responsibility for the result.
The DAT primarily tests divergent, associative linguistic creativity. It does not test the full practice of painting: selecting a subject, establishing a visual language, responding to materials, controlling composition, sustaining a body of work, or knowing when a painting is finished.
Why the human distribution matters to painters
Average performance is a statistical summary, not a description of every person. A system can exceed the mean while still falling below specialists and exceptional practitioners. That is the pattern reported in this study.
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For a working painter, the more relevant comparison is rarely software versus an average person asked to brainstorm. It may be a model versus a trained painter’s visual judgment, an AI-assisted painter versus an unaided painter, or a rapid variation versus a coherent series developed over months. Novelty, quantity of options, and originality with intention and context are different things.
AI’s ability to produce unusual combinations may affect concept development, reference gathering, and early ideation. It does not show that it can independently decide what a painting should mean or why one visual choice matters more than another.
Did AI beat humans at creative writing?
The researchers also compared models and people on haikus, film synopses, and flash fiction. The results were more qualified than a claim that AI “writes better than people.” Humans with stronger creative-writing ability outperformed the AI systems on those comparisons, although models could approach or exceed broader-population average performance on some narrower measures.
The writing participants likely represented a more skilled group than the general-population sample used for the DAT. The two human groups should not be treated as identical. Nor should a text benchmark be treated as a direct test of visual art: a surprising phrase or image does not by itself demonstrate understanding, personal purpose, or a sustained aesthetic project.
A high score does not prove human-like creativity
A benchmark score establishes that a system performs well according to that benchmark’s definition. It does not establish conscious intention or personal experience, aesthetic judgment, independent goals, understanding of cultural and social context, authorship, human-like imagination, or the ability to recognize why a work matters or sustain a creative practice.
The study’s authors caution that comparable performance does not demonstrate that language models reach their results through human-like mechanisms. The most accurate language is “generated more semantically diverse responses” or “performed better on this benchmark,” not “the AI is more creative than humans.”
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Prompting and temperature change the result
The measured creativity of a model is not a single fixed number. In the study, prompt strategies and temperature affected performance. More varied prompting could increase semantic divergence, higher temperature could make responses less predictable, and an etymology-based strategy improved associations in the reported tests.
This means the result partly measures the surrounding protocol as well as the model. A fair comparison should specify:
- The exact model version.
- The system and user prompts.
- The temperature or equivalent sampling setting.
- The number of attempts allowed.
- Whether outputs were selected after seeing the results.
- How invalid or instruction-breaking answers were handled.
For painters using image or text tools, distinguish a tool’s first output from a directed workflow involving references, constraints, selection, compositing, and extensive revision. Those are different creative activities.
Does AI make people more creative?
A model performing well by itself does not prove that people perform better when given access to it. A 2025 CHI study tested 52 pairs of human participants completing the Alternate Uses Test, with one group receiving ChatGPT-4 support and another working without AI.
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This does not prove that AI universally makes people less creative. It shows that access alone is no guarantee of better creative work. For an artist, an AI tool may open possibilities—or encourage premature acceptance of the first plausible image, narrow attention around familiar patterns, or replace slower development of a personal idea.
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Why creativity studies disagree
The 2026 study is not a final universal ranking of human and machine creativity. A separate large-scale comparison, listed by PubMed, reported that average human divergent creativity was slightly higher than LLM performance under its own design. It used 9,198 human participants and 215,542 LLM observations.
The difference does not automatically invalidate either result. Conclusions are sensitive to the test and scoring algorithm, human sample and skill distribution, model selection and version, prompt design and temperature, number of model observations, and statistical analysis. Automated metrics can make comparisons more repeatable, but repeatability does not mean one metric captures every form of creativity.
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Where AI can help
Generative tools can help with rapid exploration: producing alternative compositions, suggesting unusual combinations, developing mood-board directions, translating a verbal idea into visual possibilities, or breaking through a blank page. Their speed can make it inexpensive to explore paths a painter might otherwise dismiss too early.
Where human judgment remains central
Painting depends on decisions not captured by semantic-distance scores: what subject deserves attention; which references are appropriate and why; how color, scale, edge, texture, and light work together; what an audience or community may understand or feel; which accidents should remain; how works form a coherent body of work; and when an image is meaningful rather than merely novel.
AI can supply options, but the painter still has to establish the question, impose constraints, make selections, and carry the work through revision. Those choices are much of the creative practice itself.
A useful workflow
- Begin without the tool: Write down the subject, intention, constraints, and visual problem in your own words.
- Generate alternatives: Use AI for breadth, not as an automatic answer.
- Interrogate the results: Identify what is generic, derivative, impractical, or emotionally false.
- Select deliberately: Keep only ideas that serve the painting’s purpose.
- Rebuild through your medium: Make important decisions in drawing, paint, surface, color, and composition.
- Review the finished work: Judge its effect and integrity, not how novel the prompt or generated image seemed.
This approach treats AI as an exploratory instrument rather than the author of the work.
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How to evaluate the next “AI is creative” claim
Before accepting a headline, ask:
- What kind of creativity was tested? Divergent association, storytelling, design, music, visual art, or scientific ideation?
- Who were the humans? General participants, students, professionals, experts, or award-winning creators?
- How was success scored? Semantic distance, human judgment, originality, usefulness, audience preference, or real-world achievement?
- Which model and settings were used? Version, temperature, prompt, number of samples, and selection process all matter.
- Was the task independent or collaborative? A model producing an answer alone differs from AI assisting a person.
- Does the test predict real-world success? A benchmark result does not necessarily predict a successful painting, exhibition, product, or career.
The bottom line for creative work
The evidence supports a middle position. AI has crossed the average-human threshold on some narrow tests of divergent, language-based creativity. It has not crossed the strongest-human threshold in the 2026 comparison, and these results do not demonstrate human-like creativity.
For painters, the most consequential skill is unlikely to be producing the largest number of unusual possibilities. It is knowing which possibility is worth pursuing, what it means, how to transform it through a chosen medium, and when to refuse an easy answer. Current AI can participate in that process, but the cited studies do not show that it can replace the human practice of making those judgments.
FAQ
Did AI score higher than humans on the 2026 creativity test?
Some models, including GPT-4 in the tested setup, scored above the average human participant on the Divergent Association Task. The most creative half of participants exceeded every tested model.
What does the Divergent Association Task measure?
It asks participants to produce 10 words that are as semantically different from one another as possible. It measures one aspect of divergent, associative thinking, not overall artistic quality.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteDoes a high creativity score mean AI is creative like a person?
No. A high score shows strong performance on a particular benchmark. It does not establish human-like intention, experience, judgment, or understanding.
Does AI assistance make people more creative?
Not automatically. In a 2025 CHI study, ChatGPT-4 assistance did not improve overall Alternate Uses Test performance, and the supported group produced less elaboration.
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