Reducing AI Plagiarism Through Assessment of Higher-Order Cognitive Skills

Abstract

This study examines whether assessments focused on higher-order cognitive skills can help reduce AI-driven plagiarism in educational settings. A total of 123 participants completed three tasks of increasing complexity, aligned with Bloom's taxonomy, across four groups: control, e-textbook, Google, and ChatGPT. Results from repeated-measures ANOVA revealed that both similarity scores and AI plagiarism percentages significantly declined as task complexity increased (p < .01). The ChatGPT group initially exhibited the highest AI plagiarism rates during lower-order tasks, but their performance improved on higher-order tasks requiring analysis, evaluation, and creation. These findings highlight a clear distinction between similarity scores and AI plagiarism detection, emphasising the need for combined evaluation methods. Overall, the study demonstrates that designing assessments to foster higher-order thinking offers an effective strategy for minimising plagiarism associated with generative AI tools, providing practical implications for academic integrity policies and instructional design.