Abstract A key psychological factor shaping students’ approach to problem-solving is their need for cognition—their drive to engage in and enjoy mentally demanding tasks. Students with a lower need for cognition may favour more structured or straightforward methods for solving problems. This study investigates the role of using Artificial Intelligence in solving economic problems by non-AI expert students, examining the effects of Cognitive reflection, the Need for cognition, and creativity on problem-solving performance. Results show that students with high Cognitive reflection and Need for cognition scores performed better, relying less on using Artificial Intelligence tools, particularly when satisfied with completing complex tasks. Students trusted Artificial Intelligence more when their reflective thinking and task satisfaction were lower, aligning with findings on trust transfer between users and Artificial Intelligence systems. Creativity has no influence on AI effectiveness, with students’ success depending on how well they structure Artificial Intelligence prompts. While Cognitive reflection and the satisfaction of completing complex tasks contribute to positive outcomes in solving economic problems, the introduction of Artificial Intelligence led to a decrease in student performance. As generative Artificial Intelligence tools become more common in educational contexts, it is crucial to understand how these cognitive preferences influence the effectiveness of AI-driven problem-solving environments.
Moșoi et al. (2025) studied this question.