Investigation into the Effectiveness of AI-Powered Plagiarism Detection in Universities in Jalingo Local Government Area, Taraba State
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- NGN 5000
Background of the Study
Plagiarism in academic work remains a significant challenge in educational institutions worldwide, undermining the integrity of the learning process and devaluing scholarly output (Mohammed & Adamu, 2023). With the advancement of technology, universities have begun incorporating Artificial Intelligence (AI)-powered plagiarism detection tools to combat this issue. These tools analyze student submissions for similarities with vast databases, offering a more effective way to identify plagiarism compared to traditional manual methods. In Jalingo Local Government Area (LGA), Taraba State, universities have increasingly adopted AI-based plagiarism detection systems as part of their academic integrity measures. However, the effectiveness of these systems in preventing and identifying plagiarism has not been comprehensively explored in this region.
The introduction of AI-driven plagiarism detection systems offers several benefits, including efficiency, accuracy, and the ability to check large volumes of text against a wide range of sources (Adebayo & Kazeem, 2024). These systems utilize machine learning algorithms to continuously improve their ability to detect plagiarized content, thus making them more effective over time. However, there are concerns regarding the limitations of AI in understanding context, citation nuances, and complex paraphrasing techniques. This study will investigate the effectiveness of AI-powered plagiarism detection in universities in Jalingo, Taraba State, to determine their impact on maintaining academic integrity.
Statement of the Problem
While AI-powered plagiarism detection systems have been integrated into many Nigerian universities, their effectiveness in identifying plagiarism in Jalingo, Taraba State, remains unclear. Concerns about the accuracy of these tools, especially in detecting properly cited sources or cases of self-plagiarism, highlight the need for a critical investigation. This study aims to assess how well AI plagiarism detection tools perform in Jalingo universities and their impact on promoting academic honesty.
Objectives of the Study
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To assess the effectiveness of AI-powered plagiarism detection tools in universities in Jalingo, Taraba State.
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To examine the accuracy of AI plagiarism detection tools in identifying different types of plagiarism (e.g., direct, paraphrasing).
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To explore the perceptions of students and academic staff regarding the use of AI-powered plagiarism detection systems.
Research Questions
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How effective are AI-powered plagiarism detection tools in identifying plagiarism in university assignments in Jalingo?
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How accurate are AI plagiarism detection tools in detecting different forms of plagiarism (e.g., direct copying, paraphrasing)?
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What are the perceptions of students and academic staff on the use of AI-powered plagiarism detection systems in Jalingo universities?
Research Hypotheses
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AI-powered plagiarism detection tools are effective in identifying plagiarism in university assignments in Jalingo.
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AI-powered plagiarism detection tools show high accuracy in detecting paraphrased content in university submissions in Jalingo.
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Students and staff have positive perceptions of the effectiveness of AI-powered plagiarism detection systems in promoting academic integrity.
Significance of the Study
This study will provide valuable insights into the effectiveness of AI-powered plagiarism detection in Nigerian universities, particularly in Jalingo, Taraba State. The findings will help university administrators and policymakers assess the adequacy of current plagiarism detection systems and inform future strategies for promoting academic integrity.
Scope and Limitations of the Study
The study will focus on universities in Jalingo, Taraba State, evaluating the effectiveness of AI-powered plagiarism detection tools. It will not include universities in other regions or examine non-AI-based plagiarism detection methods. Limitations include possible biases in self-reported data from students and staff, and the challenges of obtaining a wide range of academic submissions for testing.
Definitions of Terms
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AI-Powered Plagiarism Detection: The use of artificial intelligence to detect plagiarized content in academic submissions by comparing them to extensive databases of academic work.
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Plagiarism: The act of copying or presenting someone else's work, ideas, or words without proper attribution.
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Academic Integrity: The commitment to honesty and ethical behavior in academic work, including the avoidance of plagiarism.
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