Reducing AI plagiarism through assessment of higher-order cognitive skills.
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| Title: | Reducing AI plagiarism through assessment of higher-order cognitive skills. |
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| Authors: | Toker, Sacip1 (AUTHOR) sacip.toker@atilim.edu.tr, Akgun, Mahir2 (AUTHOR) |
| Source: | Innovations in Education & Teaching International. Oct2025, Vol. 62 Issue 5, p1665-1681. 17p. |
| Subject Terms: | *Bloom's taxonomy, *Generative artificial intelligence, *Instructional systems design, *Plagiarism, *Critical thinking, *Educational evaluation, *Education ethics, *Cognitive load |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Innovations in Education & Teaching International is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 14703297 |
| DOI: | 10.1080/14703297.2025.2514242 |
