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Neither AI nor people are more creative in every situation. Generative AI can produce many candidate ideas quickly and has scored strongly on some structured divergent-thinking tests. But findings across studies conflict, and producing ideas is not the same as recognizing which are original, useful, or appropriate. People bring context, purpose, and judgment that matter when a creative task reaches beyond a well-defined prompt. The practical distinction is not “AI or human,” but which part of the creative work each can contribute to—and how the result will be evaluated.
Is AI more creative than humans?
There is no research-backed universal winner. Some experiments find that language models outperform their human comparison groups on particular idea-generation tasks; a meta-analysis found no statistically significant overall advantage for AI, and a large-scale comparison found slightly higher average human performance on its task. Those findings are not mutually exclusive: they measure different tasks, samples, and scoring approaches.
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“Creativity” can mean generating unusual possibilities, solving a constrained problem, writing an evocative story, or selecting an idea that fits a real audience. A result on one measure cannot stand in for all of those abilities. It is more accurate to ask what a system or person did well in a specified task than to treat creativity as one score.
That distinction also limits what the studies can establish. They evaluate outputs under experimental conditions; they do not settle whether AI creates in the same philosophical or social sense as a person, or how it performs across every professional and artistic setting.
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What the studies actually measured
Several comparisons used divergent-thinking tasks, which ask participants to generate possibilities rather than converge on one correct answer. Examples in the evidence include the Alternative Uses Task, the Consequences Task, and the Divergent Associations Task. Other work tested convergent thinking, problem solving, or creative writing. These are related but distinct capabilities, so a strong result in one should not be generalized automatically to another.
Scoring matters, too. Counting ideas captures fluency, but not necessarily variety, originality, relevance, or the ability to select the best idea. A system can produce a long list while struggling to tell conventional suggestions from genuinely unusual ones.
What does the evidence say about AI idea generation?
The findings depend on the study design. One 2024 experiment found higher GPT-4 scores than its human sample on three divergent-thinking measures. A 2025 meta-analysis pooling 17 studies found no statistically significant overall advantage for prompted generative AI in creative idea generation.
| Study | Comparison and scope | Reported finding |
|---|---|---|
| Hubert, Awa, and Zabelina, 2024 | GPT-4 compared with 151 people on the Alternative Uses Task, Consequences Task, and Divergent Associations Task | GPT-4 scored higher on each measure and was more original and elaborate when fluency was controlled. |
| De Rooij and Biskjaer, 2025 meta-analysis | 15 articles, 17 studies, and 115 effect sizes on generative AI and creative idea generation | The pooled effect favoring prompted generative AI was small and not statistically significant; the authors found no empirical support for an overall claim that it had surpassed people. |
| Wang, Huang, Shen, and Uzzi, published online in 2025 and in the 2026 issue of Nature Human Behaviour | 9,198 human participants and 215,542 LLM observations on an established divergent-idea task | The authors reported slightly higher average human creativity, greater variation among people, and higher human performance at the high end. |
| 2025 egg-task experiment | ChatGPT-4o compared with 47 people and aggregated results from eight prior studies using the same procedure | The model produced more ideas, but showed fixation bias and limited ability to distinguish original from conventional ideas. |
The first comparison supports a narrow claim about GPT-4 on those three measures and under that experiment’s design. It does not establish that AI leads in every kind of brainstorming, much less in all creative work. The meta-analysis addresses the broader idea-generation question by combining evidence across studies; its result is that an overall advantage was not established, not that every individual study found identical performance.
The large-scale study adds a different consideration: average score and range are not the same thing. Its authors reported that people varied more and that human results were higher at the right-hand tail of the distribution. This describes the task and sample examined; it does not prove that people outperform models in every field or that a model cannot produce an exceptional result.
Why apparently conflicting results can all be true
- Different tasks: A model may excel at one kind of idea-generation test without matching people in writing or another discipline.
- Different scoring: A study may emphasize fluency, originality, elaboration, or a combined score. Those measures answer different questions.
- Different samples and comparisons: Results depend on which people and systems are tested and how the comparison is constructed.
- Different interpretations: Even strong test performance does not by itself establish broad creative ability outside that test.
The careful reading is therefore not to average every headline into a simple “tie.” Instead, keep each finding attached to its task, measure, and comparison group. The evidence supports meaningful AI performance in some structured tasks, but does not support a blanket conclusion that AI has surpassed people overall.
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What can AI do better than humans creatively?
In some tested settings, AI has been able to generate many plausible options and score highly on divergent-thinking assessments. Its clearest practical advantage in that role is helping expand the initial set of candidates, especially when the task is clearly described. That is a useful contribution, but it is not the same as making the final creative decision.
Generating alternatives at speed and scale
The 2025 egg-task experiment found greater productivity from ChatGPT-4o than from the human comparison in that procedure. This supports a bounded claim about output volume in the experiment; it does not supply a universal production rate or show that every additional suggestion is valuable. More options can be helpful when a person needs starting points, variations, or directions to consider.
Structured prompts can also make the assignment legible: propose possible uses, consequences, or associations under a defined task. The GPT-4 study’s results on three divergent-thinking measures show that a language model can perform well on tests designed to elicit multiple ideas. They do not establish that it can independently identify which problem deserves attention or what a particular community will value.
Working as a source of candidate ideas
A separate 2024 discussion paper reported that chatbot ideas were rated more creative than ideas from a representative sample of US adults in the tasks it studied. It also reported that human-AI augmentation improved human creativity, though not as much as AI-alone ideas in that experiment. These results are one study’s findings, not a general verdict about collaboration or a guarantee that an AI suggestion will improve a particular project.
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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 problemsIn practice, AI can be useful when the next step is to broaden a well-specified search: list possible approaches, vary a premise, or suggest alternatives to a draft idea. A person still needs to decide whether the suggestions address the real brief and whether they are distinct enough to merit attention.
Where do people retain an advantage?
People remain important where creative work requires more than producing options: choosing a direction, understanding a particular context, and deciding whether a result is suitable. The studies especially caution against confusing idea volume with the ability to recognize originality.
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Recognizing what is genuinely original
In the 2025 egg-task experiment, ChatGPT-4o produced more ideas but showed fixation bias: many suggestions remained within conventional categories. The researchers also reported limited ability to distinguish original ideas from conventional ones, unlike the people in the comparison. This makes a review step valuable when novelty is a goal, rather than assuming that the longest or most surprising-looking list contains the strongest ideas.
The study authors concluded that human involvement remains necessary, particularly in evaluating and filtering generated ideas. That is their interpretation of the findings, not proof that every AI output in every setting requires the same amount or kind of review. The practical point is narrower: in a task where originality matters, the experiment found a gap between producing ideas and judging them.
Bringing a brief and its audience into the decision
A creative result is judged against a purpose. It may need to suit a specific audience, fit a cultural setting, respect an ethical constraint, or solve a real-world problem. The cited studies do not establish that a language model can replace human responsibility for those contextual decisions. A person who knows the brief can assess whether a technically plausible suggestion actually belongs in the work.
This does not require treating human judgment as infallible. People can also miss promising possibilities or favor familiar ideas. Rather, generation and evaluation are separate jobs: AI can contribute candidates, while people bring the goals and contextual knowledge needed to decide what to pursue.
How do AI and human results differ across creative tasks?
They do not differ in one consistent direction across domains. One 2025 study reported that ChatGPT-4o, DeepSeek-V3, and Gemini 2.0 outperformed its human sample on an Alternate Uses Task and a Remote Associates Test, while a separate study across 13 creative tasks placed LLMs at the 46th percentile overall, with stronger performance in divergent thinking and problem solving and weaker performance in creative writing.
| Assessment or study | What it examined | Reported result |
|---|---|---|
| 2025 study of ChatGPT-4o, DeepSeek-V3, and Gemini 2.0 | Alternate Uses Task and Remote Associates Test; the study included 46 people and 57 Remote Associates Test items. | All three systems outperformed the study’s human sample on those assessments. The authors questioned whether current assessment methods are appropriate for measuring AI creativity. |
| 2025 study spanning 13 creative tasks | LLM performance compared with human participants across a range of creative tasks | LLMs scored at the 46th percentile overall, with stronger results in divergent thinking and problem solving and weaker results in creative writing. |
The first study’s result should remain attached to its two assessments and its sample. Its authors’ question about measurement is important: a test developed to assess people may not capture every feature of model performance in the same way. That concern does not erase the reported scores, but it does limit how much those scores can tell us about creativity in general.
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The 13-task comparison makes the domain difference especially clear. Stronger performance in divergent thinking and problem solving coexisted with weaker performance in creative writing. If the reader’s real question concerns a specific activity, results from a different activity are not a reliable substitute.
A useful comparison framework
When comparing a person’s work with AI output, separate the dimensions being judged:
- Task structure: Is there a clear prompt and a defined kind of answer, or does the work require finding a new direction?
- Output volume: How many candidate ideas are produced, and how many are usable?
- Originality and variety: Are suggestions genuinely distinct, or do they cluster around conventional responses?
- Evaluation: Can the creator identify the most original, relevant, and appropriate option?
- Context and purpose: Does the idea fit its intended audience, setting, constraints, and real-world goal?
- Creative domain: Are you assessing brainstorming, problem solving, writing, or another discipline?
This framework synthesizes the differences among the study designs; it is not a single tested scale. It helps prevent a narrow score—such as the number of ideas—from becoming a claim about all the qualities that make creative work effective.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should you use AI for brainstorming?
AI can be a useful source of candidate ideas when the task is well specified and a person will assess the result. The evidence supports trying it as a way to widen the option set, not treating its output as a finished creative judgment or a replacement for human responsibility.
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- Define the actual brief. State what the ideas are for, the audience or situation they should address, and any constraints that matter. A defined task makes it easier to judge whether a suggestion is relevant.
- Use AI to produce alternatives. Ask for candidate directions rather than a single definitive answer. The aim is to create options that can be considered, not to assume that fluency proves originality.
- Look for fixation and repetition. Check whether the suggestions differ meaningfully or keep returning to familiar categories. The egg-task study found that high output could coexist with conventional ideas.
- Evaluate ideas against the brief. A person should assess novelty, relevance, and fit, particularly when originality is central to the assignment. Do not rely on the model alone to label its own output as original.
- Develop the chosen direction. Use human judgment to select, combine, revise, or reject candidates. Keep the final decision tied to the purpose and constraints of the work.
This is a practical application of the evidence, not a workflow that the studies prove is best for every profession. The cited work identifies both AI’s capacity to generate ideas and a weakness in judging their originality; how much review is appropriate depends on the task and the consequences of getting it wrong.
When the approach is a poor fit
- The task depends on a kind of creative performance the relevant evidence does not cover, such as a specific professional or artistic setting.
- The goal is to discover which problem matters, rather than generate responses to a problem already defined.
- A suggestion must meet a contextual or ethical constraint that the prompt may not fully express.
- The decision-maker intends to treat a high idea count as proof of high originality or quality.
In those cases, the limitation is not simply that the model may generate a weak suggestion. It is that the requested task depends on evaluation, context, or domain-specific performance that idea-generation scores alone do not establish.
Does a high creativity score prove that AI is creative like a person?
No. A test score shows how a system performed under that assessment’s design and scoring rules. It does not settle what creativity means in a broader philosophical or social sense, nor does it show that test performance transfers to every creative practice.
The studies in this evidence base concentrate on experimental tasks rather than the full range of professional or artistic work. The 2025 study that compared models on an Alternate Uses Task and a Remote Associates Test also questioned whether current assessment methods are appropriate for measuring AI creativity. Scores remain informative, but the interpretation should stay close to what was actually tested.
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Frequently Asked Questions
Can AI have original ideas?
The cited experiments can assess whether an output is judged original under a particular task and scoring method, but they do not resolve whether AI has originality in the same sense as a person. In the egg-task comparison, the model had difficulty distinguishing its original suggestions from conventional ones.
Why do AI creativity studies reach different conclusions?
They use different tasks, samples, models, and scoring methods, so a result on idea fluency may not match a result on writing or idea selection. A meta-analysis combines findings across studies, but still cannot make unlike tasks identical.
Does prompt engineering reliably make AI more creative?
The large-scale comparison found mixed-to-negative results from strategic prompt engineering, while attempts to use “genius” personas or demographic roles had a threshold. The finding concerns that study’s task and prompting approaches, not every possible prompt or creative project.
Do the studies show that AI-human collaboration always beats either one alone?
No. One 2024 discussion paper reported that human-AI augmentation improved human creativity in its studied tasks, but the augmented ideas did not score as highly as AI-alone ideas in that experiment. That result cannot establish a general rule for collaboration.
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