This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Knowledge workers such as scientists, teachers, lawyers and business leaders are increasingly using generative artificial intelligence applications for tasks that demand imagination, creativity and problem-solving. Ironically, perhaps, recent research shows that people who interact with generative AI for work tasks can end up not thinking critically.
They can surrender to AI's quick and confident answers, which can lead to speedy but low-quality solutions. Scholars refer to this increasingly prevalent problem as cognitive offloading. And they are showing that it can erode the very skills that make knowledge workers valuable.
My research at the intersection of artificial intelligence and knowledge work provides a different view, however. In one study, my colleagues and I interviewed 45 knowledge workers at a U.S. public university. They all were using generative AI applications in their core tasks: generating new ideas, solving complex problems and designing novel solutions.
We also analyzed the prompts and outputs from their iterative AI chats. We found that when knowledge workers intentionally use AI to challenge their ideas—to generate friction—they can significantly improve their performance. Previous research on human-AI collaboration for loan evaluations had similar results.
Using AI to find information that contradicts your own thinking can reveal unlikely perspectives that disrupt your familiar logic and spark new insights. This is particularly important because AI models quickly become familiar with how a user prompts the model and interprets its output, and the model presents information according to these patterns. For example, a marketing professional in our study asked AI to create virtual customers with personas that would lead them to dislike a product under development, which was an accounting certificate program.
The contrary feedback from the AI model revealed customer preferences that the accounting professional designing the program had not considered, such as using multiple cases from different industries to illustrate a principle rather than cases from the same industry. An attorney used AI to find obscure legal loopholes and to consider how companies might exploit them. That exposed surprising but realistic scenarios of hard-to-detect unethical behavior in employees.
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