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Short answer: Neither AI nor people win every creative task. Generative AI can produce large numbers of plausible options quickly and performs strongly on some structured divergent-thinking tests. Humans remain better positioned to define meaningful problems, understand context, recognize unusual value, and decide what is appropriate to pursue. The most defensible use of AI is as an idea-expansion partner, with human judgment controlling direction and selection.
Is AI more creative than humans?
That question has no single, evidence-based yes-or-no answer because “creativity” is not one measurable ability. A test of how many unusual uses someone can list for an object is different from writing a compelling story, identifying an unsolved customer problem, composing music for a particular culture, or deciding whether an idea is ethically acceptable.
Results change with at least six variables:
- Task structure: A tightly specified prompt is easier to compare than an open-ended brief in which the creator must decide what problem matters.
- Output volume: Producing many candidates quickly is not the same as producing one valuable idea.
- Originality: An unusual answer may still be a familiar variation once the wider context is considered.
- Evaluation: Generating options and recognizing the strongest option are separate skills.
- Context: Audience, culture, timing, constraints, and consequences affect whether an idea is useful.
- Domain: Brainstorming, problem-solving, writing, visual design, and scientific hypothesis generation place different demands on a creator.
Some experiments find higher AI scores than human samples on particular divergent-thinking tasks. Other work finds no overall advantage, slightly higher average human performance, or weaker AI results in creative writing. These findings are compatible: they measure different slices of creative work rather than a universal “creativity score.”
What the main studies found
GPT-4 performed strongly on three divergent-thinking tasks
In a 2024 experiment by Hubert, Awa, and Zabelina, GPT-4 was compared with 151 people on the Alternative Uses Task, Consequences Task, and Divergent Associations Task. GPT-4 scored higher on all three measures and was judged more original and elaborate when fluency was controlled. The authors described this as evidence of higher creative potential in the specific divergent-thinking tests they used.
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The result does not show that GPT-4 can independently choose a worthwhile goal, understand a social situation, or deliver superior work in every creative profession. It shows that, under that test design, the model generated responses that scored well on defined measures of idea production.
A meta-analysis found no significant overall advantage
De Rooij and Biskjaer’s 2025 meta-analysis combined 15 articles, 17 studies, and 115 effect sizes on generative-AI idea generation. The pooled effect favoring prompted AI was small and not statistically significant. The authors found no empirical support for the broad claim that generative AI had surpassed humans overall.
They also caution that laboratory task outputs are not automatically equivalent to human creativity as it appears in a cultural, situated, value-driven setting. A score on an idea-generation exercise is useful evidence about that exercise, not a complete theory of creativity.
A very large comparison favored human averages and extremes
Wang, Huang, Shen, and Uzzi analyzed 9,198 human participants and 215,542 large-language-model observations in an established divergent-idea task. They reported that human creativity was slightly higher on average, that humans showed more variation, and that people performed better at the high end of the score distribution.
The same work tested attempts to raise model performance with “genius” personas and demographic roles. Those interventions had a threshold rather than producing unlimited gains. Strategic prompt engineering produced mixed-to-negative results in the study. These conclusions apply to the task and samples examined, not to every model or field.
ChatGPT-4o produced more ideas but showed fixation
A 2025 egg-task experiment compared ChatGPT-4o with 47 people and with aggregated results from eight earlier studies using the same procedure. The model was more productive, generating a larger number of responses. However, many of its ideas remained within conventional categories, a pattern described as fixation bias. The model also had limited ability to distinguish its original ideas from conventional ones, whereas the people in the comparison were better at that judgment.
The study authors concluded that human involvement is particularly important for evaluating and filtering generated ideas. That is a statement about the observed combination of high fluency, fixation, and weak self-evaluation; it is not proof that every AI output needs the same amount of review.
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Other tests produce different rankings
Another 2025 comparison tested ChatGPT-4o, DeepSeek-V3, and Gemini 2.0 against a human sample on an Alternative Uses Task and a 57-item Remote Associates Test. All three systems outperformed the study’s human sample on those assessments. The authors also questioned whether current creativity tests are suitable for measuring AI creativity, which limits how far the result can be generalized.
A separate 2025 study covering 13 creative tasks placed large language models at the 46th percentile against human participants overall. Models were stronger in divergent thinking and problem solving but weaker in creative writing. The single percentile is therefore less informative than the pattern across domains: model capability is uneven.
An IZA discussion paper published in September 2024 reported that chatbot ideas were rated more creative than ideas from a representative sample of US adults in its tasks. Human-AI augmentation improved human creativity in that experiment, although the augmented results did not match the AI-alone ideas. This is one study under one design, not a general verdict on collaboration.
| Evidence | Sample or scope | Reported result | What it does not establish |
|---|---|---|---|
| Hubert, Awa, and Zabelina (2024) | 151 people; GPT-4; three divergent-thinking tasks | GPT-4 scored higher on each task and on originality and elaboration after fluency control | That AI is superior in all creative domains or can set its own meaningful goals |
| De Rooij and Biskjaer meta-analysis (2025) | 15 articles, 17 studies, 115 effect sizes | No statistically significant overall AI advantage in idea generation | That every model, prompt, or task performs equally |
| Egg-task study (2025) | ChatGPT-4o; 47 people; eight prior studies | More model ideas, comparable fixation, weaker discrimination between original and conventional ideas | That all AI outputs are conventional or that human review has one fixed required form |
| Wang, Huang, Shen, and Uzzi (online 2025; 2026 issue) | 9,198 people; 215,542 LLM observations | Slightly higher human average, wider human variation, stronger human high-end scores | That the result transfers unchanged to writing, art, or professional practice |
| 13-task comparison (2025) | 13 creative tasks | LLMs at the 46th percentile overall; stronger divergent thinking and problem solving, weaker creative writing | That a single percentile captures all creative ability |
What AI does best creatively
Expanding the option set quickly
AI can produce dozens of candidate directions in seconds. That is useful when a team is stuck, when a brief has many possible approaches, or when you need inexpensive first drafts before investing human time. The advantage is throughput, not automatic quality.
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For example, a product team can ask for alternative onboarding concepts for different user groups, a teacher can request several activity formats for the same learning objective, and a writer can generate contrasting opening scenes. Each output is a candidate to inspect rather than a finished decision.
Recombining familiar material under clear constraints
Models are effective at combining requirements such as audience, tone, format, length, and topic into multiple variants. A tightly specified brief gives the system a tractable search space. Asking for a table of approaches, counterarguments, metaphors, or use cases can make differences between options easier to see.
This strength is also a risk: a model may rearrange familiar patterns while presenting them as novel. Constraint satisfaction should therefore be treated as a starting point for originality checks, not as proof of originality.
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Supporting divergent thinking and early problem solving
The strongest comparative results for AI tend to occur in tasks that reward fluency, association, and the generation of alternatives. Models can suggest adjacent applications, edge cases, analogies, and combinations that a person can then test against the actual brief.
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Making iteration cheap
A person can ask for a shorter version, a version for a different audience, a reversal of an assumption, or five radically different structures without starting from a blank page each time. This lowers the cost of exploration. It does not remove the need to compare versions, because successive outputs can drift toward the same template.
What humans do best creatively
Choosing the problem and the purpose
Human creators can notice an unmet need, reinterpret a brief, or decide that the stated problem is the wrong one. This kind of problem finding is different from responding to a prompt. It includes deciding whose interests matter, what success means, and which constraints are non-negotiable.
Applying lived and situated context
People bring experience of a particular audience, workplace, community, historical moment, and set of consequences. That context helps them detect when an apparently clever suggestion is insensitive, impractical, culturally out of place, or already familiar to the intended audience.
Recognizing valuable originality
The egg-task findings are important because generating an unusual answer and recognizing that it is unusual are separate abilities. Human reviewers can compare an idea with domain knowledge, current conventions, and the real brief. They can also judge whether novelty improves the result or merely makes it strange.
Producing high-end, varied work
The large-scale comparison found a wider human performance range and stronger human results at the high end of its task. That distribution matters in practice: a system may provide a dependable stream of adequate options, while exceptional work may depend on a person’s unusual insight, persistence, taste, or willingness to depart from the obvious path.
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Maintaining intention and accountability
A human author can explain why a choice was made, accept responsibility for its effects, and revise the goal when circumstances change. The studies summarized here do not settle whether machine output constitutes creativity in the same philosophical or social sense as human creativity. They do show why output scores alone cannot answer that question.
How to decide which approach fits a task
| Task condition | Useful first move | Primary human responsibility |
|---|---|---|
| Many options are needed under a clear brief | Ask AI for structured alternatives and variations | Remove duplicates and check fit |
| The problem itself is uncertain | Have people investigate users, context, and goals before prompting | Define the problem and success criteria |
| Novelty is critical | Use AI for remote associations and counterexamples, then compare with domain knowledge | Verify that ideas are genuinely distinct and valuable |
| Audience or cultural sensitivity is high | Use AI only for bounded options, if at all | Lead contextual, ethical, and representation decisions |
| Writing quality and voice are central | Use AI for outlines, alternatives, or editing experiments | Own the point of view, voice, factual accuracy, and final language |
| Consequences are costly or irreversible | Limit AI to low-risk exploration | Make and document the final decision |
A practical human-AI brainstorming workflow
This process uses AI where its speed is helpful while preserving a human checkpoint for purpose, originality, and appropriateness.
- Define the brief before opening a model. Write the audience, desired outcome, constraints, deadline, non-negotiable values, and what would make an idea unusable. If you cannot state the goal, ask people to investigate the problem first.
- Generate a small human baseline. Each participant writes several ideas independently. This prevents the first model response from becoming the team’s default frame and provides a comparison for conventionality.
- Request multiple categories, not one undifferentiated list. Ask for conservative, unfamiliar, low-cost, high-impact, reversible, and deliberately counterintuitive options. Require a short rationale and the assumption each option makes.
- Force variation through new angles. Change the audience, setting, resource limit, time horizon, or underlying analogy. Ask the model to identify similarities among its own suggestions so repeated patterns are visible.
- Audit for fixation. Group outputs by concept, not wording. If many answers are variations of one familiar solution, discard the duplicates and change the prompt or return to human exploration.
- Check originality outside the model. Compare finalists with known products, published work, common industry practices, and the team’s own prior ideas. Do not rely on the model to certify that an idea is new.
- Evaluate fit and consequences. Score each candidate against the brief, audience needs, feasibility, ethical constraints, accessibility, and likely unintended effects. Keep the scoring criteria visible to everyone involved.
- Develop one direction with human ownership. Use AI for variants, counterarguments, or implementation details only after a person chooses the direction. A named human owner should approve the final concept and its explanation.
- Keep a decision record. Save the brief, the options considered, the reasons for rejection, and the human edits. This makes later review possible and prevents a fluent but unsuitable suggestion from being mistaken for a validated decision.
Common failure modes and how to correct them
Confusing quantity with creativity
A long list feels productive, but volume can hide duplication. Cluster ideas by underlying mechanism, not by surface wording, and count genuinely different directions. If ten suggestions collapse into two concepts, the useful output is two concepts.
Accepting polished conventionality
Fluent language can make a familiar answer sound inventive. Ask what existing pattern the idea resembles, which assumption it changes, and what evidence would show that it is new to the intended audience. A human with relevant domain knowledge should make that comparison.
Letting the first prompt define the problem
Prompting too early can anchor a team on the model’s framing. Write a problem statement and at least one alternative framing first. Then ask the model to challenge both rather than merely elaborate one.
Using personas as a shortcut to originality
The large-scale comparison found that “genius” and demographic-role prompts had a threshold, while strategic prompt engineering produced mixed-to-negative results. A persona can change tone or perspective, but it is not a reliable substitute for better information, stronger constraints, or human judgment.
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Strong performance on an Alternative Uses Task does not predict equal performance in fiction, industrial design, scientific discovery, or brand strategy. Before applying a claim, identify the task, sample, scoring method, and domain that produced it.
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Asking AI to be the final originality judge
The egg-task study found that ChatGPT-4o had difficulty separating original from conventional ideas. Use the system to propose comparison criteria or find counterexamples, but have people with relevant context make the final originality and appropriateness judgment.
How the balance changes by creative field
Writing and editing
AI is useful for outlines, alternate structures, counterarguments, headline options, and controlled rewrites. Human authors should supply the central claim, reporting or source knowledge, voice, factual verification, and final selection. The 13-task comparison’s weaker creative-writing result is a reminder that fluent prose is not the same as distinctive authorship.
Product and service design
AI can expand feature concepts, user scenarios, and failure cases after the team has defined a real user problem. Interviews, observation, accessibility knowledge, feasibility checks, and responsibility for trade-offs remain human-led. A large option set is valuable only if it reflects the people who will use the product.
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Generative systems can provide rapid mood, composition, or interface directions, but the designer must check originality, rights, accessibility, cultural meaning, and production constraints. Treat outputs as references or prototypes unless the project has a clear policy for generated assets and attribution.
Education and facilitation
AI can create alternative examples, questions, and activity formats. Teachers and facilitators must decide whether material is age-appropriate, accurate, inclusive, and aligned with learning goals. Students benefit more when they explain, critique, and revise suggestions than when they simply accept a finished answer.
What remains unsettled
The available studies concentrate on experimental tasks rather than the full range of professional and artistic practice. They use different prompts, models, human samples, scoring rubrics, and definitions of originality. Some compare one model with one sample; others pool many studies or many model observations. Those design differences explain why apparently conflicting findings can all be valid within their stated limits.
There is also a philosophical question the tests cannot resolve: whether producing a high-scoring output is creativity in the same sense as a person having an intention, participating in a culture, and taking responsibility for a work. A behavioral score can describe what an AI system does under test conditions without settling what the activity means.
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Quick Recap
A quick decision checklist
- Have we defined the problem, audience, and success criteria independently of the model?
- Do we need many options, or one deeply informed direction?
- How will we detect duplicates and conventional answers?
- Who has the domain knowledge to judge originality and cultural fit?
- What risks require human approval before an idea is used?
- Are we evaluating the model on the same task and standard as the people we are comparing?
- Can we explain why the selected idea is valuable, rather than merely why it sounds polished?
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