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

Autor: Toker, Sacip, Akgun, Mahir
Rok vydání: 2024
Předmět:
Druh dokumentu: Working Paper
Popis: 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.
Databáze: arXiv