Abstract

This study examines how generative AI (GenAI) influences the mental effort students face when solving complex analytics problems. Using Cognitive Load Theory (CLT), we examine how GenAI tools specifically redistribute the three dimensions of cognitive load: intrinsic, extraneous, and germane. We propose that GenAI can help students manage complexity, minimize distractions, and promote deeper thinking when used effectively. To investigate this, we propose a quasi-experimental design in graduate-level business analytics courses. Students will complete both well-structured and ill-structured tasks, with one group using GenAI tools and the other relying on traditional methods. Data will be collected through surveys, task performance, and student reflections. The study seeks to determine whether GenAI merely simplifies tasks and fosters stronger learning among students or vice versa. The findings will advance theory, inform instructional design, and guide management education by positioning GenAI as a necessary tool for reducing mental load and strengthening problem-solving skills.

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