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AI can outperform the average person on some narrow creativity tests, but that is not the same as replacing painters or proving that machines are creative in the human sense. A January 2026 Scientific Reports study comparing large language models with 100,000 people found that some models exceeded the human average on divergent-thinking tasks. Yet the most creative half of the human participants outperformed every tested model, and the top 10% performed better still.
For painters and other visual creators, the practical lesson is measured rather than sensational: generative AI is becoming a powerful source of variations and visual prompts, but artistic purpose, judgment, context, revision, and meaning remain much harder to reduce to a benchmark score.
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The headline result is a split decision
The study, published in Scientific Reports on January 21, 2026, tested several large language models against a very large human dataset. Its result sounds contradictory until the human scores are viewed as a distribution rather than a single number:
- Some tested models, including GPT-4 in the study’s configuration, scored above the average human participant.
- Gemini Pro was statistically indistinguishable from human performance in the reported comparison.
- The average score of the most creative half of humans exceeded every tested model.
- The top 10% of human participants exceeded the models by a larger margin.
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 models were not intended to be 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 then estimates the distance between the words.
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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 a useful way to measure one aspect of idea generation, but semantic distance is not the same as artistic quality. The test 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 many 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 a viewer, 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 exactly the pattern reported in this study.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFor a working painter, the more relevant comparison is rarely “software versus an average person asked to brainstorm.” It is more likely to be:
- a model versus a trained painter’s visual judgment;
- an AI-assisted painter versus an unaided painter;
- a rapid image variation versus a coherent series developed over months;
- novelty versus originality with intention and context;
- quantity of options versus the ability to select, edit, and reject them.
AI’s ability to produce many 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 the broader population’s average performance on some narrower measures.
That comparison also has an important limitation: 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.
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Nor should a text benchmark be treated as a direct test of visual art. A model can produce a surprising phrase without understanding why it is surprising. Similarly, an image generator can create an unusual arrangement of color and form without having a personal reason for making it, a sustained aesthetic project, or an understanding of the experience it evokes.
A high score does not prove human-like creativity
A benchmark score establishes that a system performs well according to the benchmark’s definition. It does not establish:
- conscious intention or personal experience;
- aesthetic judgment;
- independent goals;
- understanding of cultural and social context;
- authorship or ownership of an idea;
- human-like imagination;
- the ability to recognize why a work matters;
- the ability to sustain a creative practice over time.
The study’s authors explicitly caution that comparable performance does not demonstrate that language models reach their results through human-like mechanisms. The most accurate language is therefore “generated more semantically diverse responses” or “performed better on this benchmark,” not “the AI is more creative than humans.”
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 needs to 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, the practical equivalent is to distinguish between the tool’s first output and a carefully directed workflow involving references, constraints, selection, compositing, and extensive revision. Those are different creative activities.
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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.
In that experiment, AI assistance did not improve overall performance. The AI-supported group produced significantly less elaboration, while the main measures showed no significant differences in fluency, originality, or flexibility. Participants selectively used the AI output and sometimes misjudged its usefulness. Some effects also appeared in an immediate unaided post-test.
This does not prove that AI universally makes people less creative. It shows that access alone is not a guarantee of better creative work. For an artist, an AI tool may open possibilities—or it may encourage premature acceptance of the first plausible image, narrow attention around familiar visual patterns, or replace the slower process through which a personal idea becomes specific.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why creativity studies disagree
The 2026 study should not be read as the 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. That study used 9,198 human participants and 215,542 LLM observations.
The difference does not automatically invalidate either result. It demonstrates that conclusions are sensitive to:
- the test and scoring algorithm;
- the human sample and its skill distribution;
- model selection and version;
- prompt design and temperature;
- the number of model observations;
- statistical analysis and output filtering.
Automated metrics are valuable because they make comparisons more repeatable. Repeatability, however, does not mean that one metric captures every form of creativity. A high DAT score may predict the ability to produce distant associations while saying little about whether someone can make a compelling painting.
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What the findings mean for painters
Where AI can help
Generative tools can be useful for 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 that a painter might otherwise dismiss too early.
Where human judgment remains central
Painting depends on decisions that are not captured by semantic-distance scores:
- what subject deserves attention;
- which references are appropriate and why;
- how color, scale, edge, texture, and light should work together;
- what a particular audience or community may understand or feel;
- which accidents should remain and which should be corrected;
- how separate works form a coherent body of work;
- when an image is merely novel and when it is genuinely meaningful.
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 not decorative extras; they 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: Ask what is generic, derivative, impractical, or emotionally false.
- Select deliberately: Keep only ideas that serve the painting’s purpose.
- Rebuild through your medium: Make the important decisions in drawing, paint, surface, color, and composition.
- Review the finished work: Judge the painting by its effect and integrity, not by how novel the prompt or generated image appeared.
This approach treats AI as an exploratory instrument rather than as the author of the work.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11How to evaluate the next “AI is creative” claim
Before accepting a headline, ask six questions:
- 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 is not the same as 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 the 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 studies cited here do not show that it can replace the human practice of making those judgments.
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