Researchers tested AI against 100,000 humans on creativity

The study, published in Scientific Reports (Nature Portfolio), points to a significant shift. Generative AI systems have now reached a level where they can outperform the average human on certain creativity measures. At the same time, the most creative people still show a clear and consistent advantage over even the strongest AI models.

AI Reaches Average Human Creativity Levels

Researchers evaluated several leading large language models, including ChatGPT, Claude, Gemini, and others, and compared their performance with results from more than 100,000 human participants. The findings highlight a clear turning point. Some AI systems, including GPT-4, exceeded average human scores on tasks designed to measure divergent linguistic creativity.

"Our study shows that some AI systems based on large language models can now outperform average human creativity on well-defined tasks," explains Professor Karim Jerbi. "This result may be surprising -- even unsettling -- but our study also highlights an equally important observation: even the best AI systems still fall short of the levels reached by the most creative humans."

Further analysis by the study's co-first authors, postdoctoral researcher Antoine Bellemare-Pépin (Université de Montréal) and PhD candidate François Lespinasse (Université Concordia), revealed a striking pattern. While some AI models now outperform the average person, peak creativity remains firmly human.

In fact, when researchers examined the most creative half of participants, their average scores surpassed those of every AI model tested. The gap grew even larger among the top 10 percent of the most creative individuals.

"We developed a rigorous framework that allows us to compare human and AI creativity using the same tools, based on data from more than 100,000 participants, in collaboration with Jay Olson from the University of Toronto," says Professor Karim Jerbi, who is also an associate professor at Mila.

How Scientists Measure Creativity in Humans and AI

To evaluate creativity fairly across humans and machines, the research team used multiple methods. The primary tool was the Divergent Association Task (DAT), a widely used psychological test that measures divergent creativity, or the ability to generate diverse and original ideas from a single prompt.

Created by study co-author Jay Olson, the DAT asks participants, whether human or AI, to list ten words that are as unrelated in meaning as possible. An example of a highly creative response includes words like "galaxy, fork, freedom, algae, harmonica, quantum, nostalgia, velvet, hurricane, photosynthesis."

Performance on this task is strongly linked to results on other established creativity tests used in writing, idea generation, and creative problem solving. Although the task is language-based, it goes well beyond vocabulary. It engages broader cognitive processes involved in creative thinking across many domains. The DAT also has practical advantages, as it takes only two to four minutes to complete and can be accessed online by the general public.

From Word Lists to Real Creative Writing

The researchers then explored whether AI success on this simple word association task could extend to more complex and realistic creative activities. To test this, they compared AI systems and human participants on creative writing challenges such as composing haiku (a short three-line poetic form), writing movie plot summaries, and producing short stories.

The results followed a familiar pattern. While AI systems sometimes exceeded the performance of average humans, the most skilled human creators consistently delivered stronger and more original work.

Can AI Creativity Be Adjusted?

These findings raised another important question. Is AI creativity fixed, or can it be shaped? The study shows that creativity in AI can be adjusted by changing technical settings, particularly the model's temperature. This parameter controls how predictable or adventurous the generated responses are.

At lower temperature settings, AI produces safer and more conventional outputs. At higher temperatures, responses become more varied, less predictable, and more exploratory, allowing the system to move beyond familiar ideas.

The researchers also found that creativity is strongly influenced by how instructions are written. For example, prompts that encourage models to think about word origins and structure using etymology lead to more unexpected associations and higher creativity scores. These results emphasize that AI creativity depends heavily on human guidance, making interaction and prompting a central part of the creative process.

Will AI Replace Human Creators?

The study offers a balanced perspective on fears that artificial intelligence could replace creative professionals. While AI systems can now match or exceed average human creativity on certain tasks, they still have clear limitations and rely on human direction.

"Even though AI can now reach human-level creativity on certain tests, we need to move beyond this misleading sense of competition," says Professor Karim Jerbi. "Generative AI has above all become an extremely powerful tool in the service of human creativity: it will not replace creators, but profoundly transform how they imagine, explore, and create -- for those who choose to use it."

Rather than signaling the end of creative careers, the findings suggest a future where AI serves as a creative assistant. By expanding ideas and opening new paths for exploration, AI may help amplify human imagination rather than replace it.

"By directly confronting human and machine capabilities, studies like ours push us to rethink what we mean by creativity," concludes Professor Karim Jerbi.

About the Study

The paper titled "Divergent creativity in humans and large language models" was published in Scientific Reports on January 21, 2026. The research brought together scientists from Université de Montréal, Université Concordia, University of Toronto Mississauga, Mila (Quebec AI Institute), and Google DeepMind.

Professor Karim Jerbi led the study, with Antoine Bellemare-Pépin (Université de Montréal) and François Lespinasse (Université Concordia) serving as co-first authors. The research team also included Yoshua Bengio, founder of Mila and LoiZéro, and a pioneer of deep learning, the technology behind modern AI systems such as ChatGPT.