AI boosts brainstorming and creativity, but experts face slowdowns during implementation

Scientists discover AI can make humans more creative

Artificial intelligence (AI) is commonly viewed as a technology designed to automate work and potentially replace human labor. However, new research from Swansea University offers a different perspective. The findings suggest that AI can also function as a creative collaborator that encourages exploration, engagement, and inspiration. Researchers from the University's Computer Science Department carried out one of the largest studies so far examining how people work alongside AI during creative design tasks. More than 800 participants joined an online experiment where they used an AI-supported system to design virtual cars. How AI Generated Diverse Design Ideas Rather than quietly optimizing designs behind the scenes, the system used a method called MAP-Elites to produce visual galleries filled with many different design possibilities. These galleries showed a wide spectrum of car concepts, including highly effective designs, unusual ideas, and even some intentionally flawed options. Turing Fellow Dr. Sean Walton, Associate Professor of Computer Science and the study's lead author, explained: "People often think of AI as something that speeds up tasks or improves efficiency, but our findings suggest something far more interesting. When people were shown AI-generated design suggestions, they spent more time on the task, produced better designs and felt more involved. It was not just about efficiency. It was about creativity and collaboration." Why Traditional AI Evaluation May Be Too Limited The study, published in the ACM journal Transactions on Interactive Intelligent Systems, also highlights a problem with how AI design tools are typically assessed. Standard metrics often focus on simple behaviors, such as how frequently users click on or copy AI suggestions. According to the researchers, these measures overlook important aspects of the experience, including how the technology influences people's thoughts, emotions, and willingness to explore new ideas. The Swansea researchers argue that AI systems should be evaluated using broader methods that capture these deeper effects. Understanding how AI shapes human thinking and engagement could provide a more complete picture of its impact. Why Imperfect Ideas Can Boost Creativity Dr. Walton emphasized that variety in AI-generated output played a crucial role in the experiment. "Our study highlights the importance of diversity in AI output. Participants responded most positively to galleries that included a wide variety of ideas, including bad ones! These helped them move beyond their initial assumptions and explore a broader design space. This structured diversity prevented early fixation and encouraged creative risk-taking. "As AI becomes increasingly embedded in creative fields, from engineering and architecture to music and game design, understanding how humans and intelligent systems work together is essential. As the technology evolves, the question is not only what AI can do but how it can help us think, create and collaborate more effectively."

AI boosts brainstorming but may slow the creative process

Generative AI has emerged as a powerful catalyst for creative brainstorming, yet it can slow experienced designers once the work turns toward finishing a piece. The finding reframes AI not simply as a productivity tool but as a collaborator whose benefits and drawbacks depend on where a creator stands in the creative process. Inside poster design tasks that moved from sketching ideas to producing final artwork, the tension between inspiration and execution became visible. Jinghui Hou, an assistant professor at the University of Houston (UH), linked the delay to expert habits. Less experienced designers could take AI output and move on, while veterans often stopped to revise, edit, and rebuild it. That extra cleanup matters because creative work rarely ends with a first spark, and the paper focused on what happens afterward. The researchers divided creative work into ideation, the stage of generating many possibilities before committing to one path. After that comes the harder task of choosing one option, building it out, and making it fit the brief. The team found that AI raised early-stage scores by 76% in novelty, 24% in relevance, and 97% in complexity. Those gains make sense because abundance helps when people are still searching, but abundance can become noise during finishing. Years of training can harden into expertise fixation, which keeps experts reaching for familiar routines. When AI produced images with its own logic, professionals often had to translate that output back into their practiced methods. Screen recordings showed heavier revision work, with expert designers adding elements and editing existing ones more often before settling. In the student experiment, experts using AI in implementation spent 57% more time and still reached similar creativity scores. People without deep design training kept gaining help because AI handled parts of production they had not already mastered. Instead of defending a personal routine, they could accept suggestions, borrow structure, and keep moving toward a workable result. Among lower-expertise students, implementation improved novelty, relevance, and complexity when AI arrived only during that later stage. That pattern suggests AI can lower barriers for beginners even while it frustrates people who already work from strong habits. The evidence came from two experiments: 192 students completed a lab poster task, and 120 professionals tackled a real advertising brief. One test kept conditions tight enough to separate idea generation from execution, while the other moved into real professional work. The field study also used Midjourney V6.1, a text-to-image generative AI system that creates detailed images from written prompts, allowing the authors to test whether newer models changed the basic pattern. Professional designers still slowed during implementation, spending about 14.6 extra minutes when AI entered only at that point. AI clearly expanded the number of ideas people tried, yet it did not trap them in endless indecision. Most participants still carried roughly one option into the final stage, even after exploring several machine-made possibilities first. Professionals also reported more mental stimulation during brainstorming, while feelings of overload barely moved at all. That balance helps explain why early experimentation opened the process instead of freezing it with too many options. Hou argued that the next improvement should happen in the interface, not only in the raw image generator. "We would suggest that all people embrace AI in the brainstorming stage. In the implementation stage, we find that AI is still very helpful for those ordinary people, but it creates more work for expert designers," Hou said. That advice points toward systems that adapt to users, instead of forcing every user to adapt to the system. The paper's logic extends beyond posters, because many creative jobs also move from open exploration to disciplined execution. Writing, advertising, and product work all ask people to generate options first, then narrow them into something usable. Whenever AI expands the search without disturbing a practiced routine, people are more likely to feel helped than interrupted. Once the tool begins shaping the final form, the question becomes less about talent and more about control. These results came from graphic design tasks, so they do not settle how musicians, filmmakers, or architects will respond. Real projects can loop through many drafts, and the paper simplified that mess into two clear stages for comparison. Even so, the repeated finding across students and working professionals makes the central split hard to brush aside. Future studies will need to test whether better controls, different media, or team settings can ease the expert slowdown. AI looked strongest when it expanded possibility and weakest when it collided with trained routines, which recasts creativity as a sequence. Tools may work best when beginners can lean on automation and experts can decide exactly when assistance enters. Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates.