Implementation of AI-Based Plagiarism Detection in Online Assignment Submissions for Universities in Katsina Local Government Area, Katsina State
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- NGN 5000
Background of the Study
Plagiarism has become a significant concern in higher education, undermining academic integrity and devaluing the quality of degrees awarded by universities. The proliferation of digital content and easy access to online resources have increased the likelihood of students copying and submitting unoriginal work as their own. Traditional plagiarism detection methods, such as manual review by lecturers, are time-consuming, inefficient, and prone to human errors (Adebayo & Yusuf, 2023). As universities in Katsina Local Government Area embrace online learning and digital assignment submissions, there is an urgent need for an advanced, automated solution to detect and prevent plagiarism effectively.
Artificial Intelligence (AI) offers a powerful tool for addressing the problem of plagiarism. AI-based plagiarism detection systems use machine learning algorithms, natural language processing (NLP), and database comparison techniques to identify copied content and provide detailed similarity reports (Okonkwo & Danjuma, 2024). Unlike traditional keyword-matching plagiarism checkers, AI-powered systems can detect paraphrased text, linguistic patterns, and contextual similarities, making them more accurate and reliable (Ahmed & Musa, 2024).
In Katsina Local Government Area, many universities face challenges in enforcing academic honesty due to the absence of robust plagiarism detection mechanisms. The reliance on manual reviews or outdated software leads to inconsistencies in plagiarism detection, allowing academic dishonesty to thrive (Usman & Bello, 2024). Implementing an AI-based plagiarism detection system in online assignment submissions will not only enhance academic integrity but also improve the efficiency of the grading process. This study, therefore, seeks to develop and implement an AI-driven plagiarism detection system to strengthen the assessment of academic work in universities within Katsina.
Statement of the Problem
The increasing prevalence of plagiarism in higher education poses a threat to academic integrity and the credibility of university degrees. In many universities, including those in Katsina Local Government Area, plagiarism detection is often performed manually or through basic online tools with limited effectiveness (Akinpelu & Yusuf, 2023). These methods are inadequate for detecting paraphrased or AI-generated content, allowing students to bypass plagiarism checks and submit copied work without detection.
Furthermore, universities struggle with large volumes of online assignment submissions, making it difficult for lecturers to review all assignments for originality thoroughly. This leads to inconsistencies in plagiarism detection, where some cases go unnoticed while others are wrongly flagged due to human bias or inefficient detection techniques (Okoro & Danjuma, 2024). Additionally, existing plagiarism detection tools often lack integration with university learning management systems (LMS), making them inconvenient for both students and lecturers.
AI-based plagiarism detection presents a more efficient and accurate solution by leveraging machine learning and NLP to analyze text for similarity patterns, context, and originality. Implementing such a system will ensure fair academic assessment, enhance students’ understanding of ethical research practices, and improve the overall quality of education. This study aims to implement and evaluate an AI-based plagiarism detection system tailored to universities in Katsina Local Government Area.
Objectives of the Study
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To develop and implement an AI-based plagiarism detection system for online assignment submissions in universities.
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To assess the accuracy and efficiency of the AI-based system in detecting various forms of plagiarism, including paraphrased and AI-generated content.
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To evaluate user perceptions and acceptance of the AI-powered plagiarism detection system among students and lecturers.
Research Questions
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How effective is the AI-based plagiarism detection system in identifying different types of plagiarism?
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What impact does the AI-based plagiarism detection system have on academic integrity and assessment efficiency?
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How do students and lecturers perceive the usability and reliability of the AI-powered plagiarism detection system?
Research Hypotheses
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The AI-based plagiarism detection system significantly improves the accuracy of plagiarism identification in online assignments.
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The implementation of AI-based plagiarism detection enhances academic integrity by reducing instances of copied assignments.
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The AI-based plagiarism detection system is positively perceived by students and lecturers in terms of usability and effectiveness.
Significance of the Study
This study is significant as it introduces a technologically advanced solution for ensuring academic integrity in universities. By implementing AI-powered plagiarism detection, the study will provide an efficient and accurate approach to identifying and preventing plagiarism in online assignment submissions. University administrators, lecturers, and students will benefit from a more reliable assessment process that upholds fairness and originality. The findings will also serve as a reference for other institutions seeking to adopt AI-driven academic integrity solutions.
Scope and Limitations of the Study
The study focuses on the design, implementation, and evaluation of an AI-based plagiarism detection system for online assignment submissions in universities in Katsina Local Government Area, Katsina State. The research will assess the system’s effectiveness in detecting different forms of plagiarism and its impact on academic integrity. The study does not cover other applications of AI in education, such as automated grading or personalized learning. Potential limitations include resistance to technology adoption, accuracy limitations in detecting highly sophisticated plagiarism, and integration challenges with existing university systems.
Definitions of Terms
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Plagiarism: The act of copying or using another person’s work without proper acknowledgment or citation.
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Artificial Intelligence (AI): A branch of computer science that enables machines to perform tasks that typically require human intelligence, such as text analysis and pattern recognition.
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Natural Language Processing (NLP): A field of AI that focuses on the interaction between computers and human language to understand, interpret, and process textual data.
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