Filters

20252
Reset filters

Settings

Subject: Bloom’s Taxonomy

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Reducing AI Plagiarism Through Assessment of Higher-Order Cognitive Skills
    (Routledge Journals, Taylor & Francis Ltd, 2025) Toker, Sacip; Akgun, Mahi
    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.
  • Thumbnail Image
    Conference Object
    Short-Term Gains, Long-Term Gaps: The Impact of GenAI and Search Technologies on Retention
    (Springer International Publishing AG, 2025) Akgun, Mahir; Toker, Sacip
    The rise of Generative AI (GenAI) tools, such as ChatGPT, has transformed how students access and engage with information, raising questions about their impact on learning outcomes and retention. This study investigates how GenAI (ChatGPT), search engines (Google), and e-textbooks influence student performance across tasks of varying cognitive complexity, based on Bloom's Taxonomy. Using a sample of 123 students, we examined performance in three tasks: [1] knowing and understanding, [2] applying, and [3] synthesizing, evaluating, and creating. Results indicate that ChatGPT and Google groups outperformed the control group in immediate assessments for lower-order cognitive tasks, benefiting from quick access to structured information. However, their advantage diminished over time, with retention test scores aligning with those of the e-textbook group. For higher-order cognitive tasks, no significant differences were observed among groups, with the control group demonstrating the highest retention. These findings suggest that while AI-driven tools facilitate immediate performance, they do not inherently reinforce long-term retention unless supported by structured learning strategies. The study highlights the need for balanced technology integration in education, ensuring that AI tools are paired with pedagogical approaches that promote deep cognitive engagement and knowledge retention.