ARTIFICIAL INTELLIGENCE-RELATED PLAGIARISM IN EDUCATION: A SYSTEMATIC REVIEW

Daniel L. Mpolomoka, Musonda Luchembe, Christine Mushibwe, Moono Muvombo, Moses Changala, Rex L. Sampa, Selina Banda

Abstract


The rapid advancements in artificial intelligence (AI) have transformed educational practices, but they have also introduced challenges such as AI-related plagiarism, which undermines academic integrity. This study explores the prevalence, challenges, and effectiveness of AI-based tools in detecting and preventing plagiarism in educational settings. The study employed Social Learning Theory (Bandura, 1977) to explore the behavioral dynamics influencing plagiarism and the role of institutional policies and AI tools in shaping ethical academic practices. Using a systematic review approach, 11 studies were analyzed to evaluate existing tools and strategies. The findings indicate that while AI-driven plagiarism detection tools have improved in identifying traditional and AI-generated plagiarism, they often lag behind the rapidly advancing AI technologies. Additionally, the study highlights the gaps in awareness and policy integration, emphasizing the importance of combining technological solutions with education on ethical academic behavior. The conclusion calls for a multifaceted approach that integrates technological innovation, policy frameworks, and proactive education to combat plagiarism effectively. This study contributes to the academic discourse by providing actionable insights for educators, policymakers, and developers, addressing a critical aspect of AI’s role in modern education.

 

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AI-related plagiarism, academic integrity, plagiarism detection tools, Social Learning Theory, ethical academic practices

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References


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DOI: http://dx.doi.org/10.46827/ejes.v12i4.6029

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