How and For Whom Using Generative AI Affects Creativity: A Field Experiment

What is it about?

This study examines whether using large language model (LLM)-based generative AI tools like ChatGPT influences employee creativity at work. And if they do—how, and for whom? These are urgent questions that require rigorous, real-world investigation—precisely what this field experiment set out to address. This research shows that generative AI doesn’t automatically make people more creative. Its effectiveness depends on how employees engage with the tool. Employees who actively analyze their tasks, monitor their thinking, and adjust their approach—what is termed as metacognitive strategies—are significantly more likely to use generative AI in ways that foster creativity. This is because these individuals are better at using AI to access “cognitive job resources,” the raw materials that facilitate creative thinking.

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Photo by Steve Johnson on Unsplash

Photo by Steve Johnson on Unsplash

Why is it important?

1. Theoretical Contributions: This research addresses the crucial questions of whether, how, and for whom LLM assistance enhances creativity—a key driver of innovation. It demonstrates that LLM assistance boosts workplace creativity by providing cognitive job resources, but this effect is conditional on employees’ metacognitive strategies. By uncovering the mechanisms (how) and boundary conditions (for whom) of AI-enhanced creativity, this work introduces a novel theoretical framework for turning AI investments into employee creativity. 2. Methodological Rigor: This field experiment in a tech consulting firm ensures both internal and external validity. The triangulated measures of creativity (supervisor + external ratings) minimize bias and enhance robustness. 3. Practical Impact: The findings provide practical guidance for organizations considering LLM deployment to boost employee creativity. First, the findings show that LLMs enhance employee creativity by providing cognitive job resources (e.g., access to knowledge, opportunities for task-switching/mental breaks), suggesting that organizations should leverage LLMs to boost these cognitive job resources and encourage employees to actively utilize them for enhanced creativity. Second and more critically, the findings highlight the enabling role of employees’ metacognitive strategies in leveraging LLMs to acquire these resources. Organizations should therefore consider employees’ metacognitive abilities when implementing LLMs and invest in developing these abilities through training. Finally, the study’s implications extend beyond the corporate landscape and encompass education and policy formation. Specifically, schools and universities must embed metacognitive skill development in curricula to prepare future workers for an AI-rich future. Historically, educational systems have placed heavy emphasis on knowledge acquisition and cognitive skills, frequently overlooking the importance of metacognitive abilities. These metacognitive skills will become increasingly significant as AI tools become integral to daily work processes.

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