The Role of Task Complexity in Reducing AI Plagiarism: A Study of Generative AI Tools

TL;DR

This study investigates whether designing assessments that require higher-order thinking can curb AI-driven plagiarism when students have access to generative AI tools. Using Bloom's revised taxonomy, researchers implemented three tasks across four groups (control, e-textbook, Google, ChatGPT) in a true-experimental pre-post design, assessing both traditional similarity and AI-generated content with Turnitin. Results show that AI plagiarism declines as task complexity increases, though similarity scores can be higher in groups with broader tool access, and ChatGPT exhibits the highest AI plagiarism at lower complexities. The findings underscore the need to use AI plagiarism scores alongside conventional similarity checks and advocate for assessment designs that emphasize creation, evaluation, and real-world problem-solving to deter AI-assisted dishonesty.

Abstract

This study investigates whether assessments fostering higher-order thinking skills can reduce plagiarism involving generative AI tools. Participants completed three tasks of varying complexity in four groups: control, e-textbook, Google, and ChatGPT. Findings show that AI plagiarism decreases as task complexity increases, with higher-order tasks resulting in lower similarity scores and AI plagiarism percentages. The study also highlights the distinction between similarity scores and AI plagiarism, recommending both for effective plagiarism detection. Results suggest that assessments promoting higher-order thinking are a viable strategy for minimizing AI-driven plagiarism.

Figures (4)

• Figure 1: Tasks used in the study
• Figure 2: Group formation
• Figure 3: Similarity scores
• Figure 4: AI Plagiarism scores