A 91视频 researcher analyzed the nuances of how generative AI can influence creativity. (Credit Getty Images)
Art has been around for centuries, but in the age of artificial intelligence, one 91视频 researcher is examining if generative AI helps or hurts creativity.
Jinghui Hou, lead author and assistant professor in the C. T. Bauer College of Business, began when the applications first became popular. Hou along with co-authors from around the world analyzed the nuances of how generative AI can influence creativity, depending on the context, in a paper in Information Systems Research.
Researchers identified two distinct stages of creativity: ideation and convergent thinking. They define the ideation stage as brainstorming what to create.
鈥淚n the first stage we find that for anyone, including ordinary people and expert designers, AI is very helpful because of its computational power,鈥� Hou said. 鈥淚t can go beyond the imagination that humans have. For example, if I wanted to imagine a tiger with wings, it would be hard to see that in my head, but AI can do it easily.鈥�
In the stage of convergent thinking, they found that AI impacts creative professionals differently than designers with lower levels of expertise. Following ideation, convergent thinking demonstrates putting the technology into action to create art.
鈥淚n the implementation stage, we find that AI is still very helpful for those ordinary people, but it creates more work for expert designers,鈥� Hou said. 鈥淭his is because the designer has years of training to materialize a piece of artwork. We find that AI uses different techniques to produce creative work. For designers, it can become burdensome to revise what AI made.鈥�
The business implications of the findings suggest that creatives and generative AI developers should carefully consider how to best incorporate technology into the production process, Hou said.
鈥淲e would suggest all people embrace AI in the brainstorming stage,鈥� she said. 鈥淲e would suggest the designers of AI have the product more tailored to the different users in the implementation stage. It could give users more freedom to fit the technology to their usage pattern and workflow. In a sense, it's not about people catering to the AI, but the AI technology catering to people.鈥�