Comparative Study of AI-Based and Traditional Methods for Detecting Plagiarism in Kaduna Polytechnic, Kaduna State
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- NGN 5000
Background of the Study
Academic integrity is a cornerstone of the educational system, and the growing prevalence of plagiarism in student submissions poses significant challenges to educational institutions worldwide. At Kaduna Polytechnic, Kaduna State, traditional methods of detecting plagiarism, such as manual checks and simple software tools, have limitations in accurately identifying advanced forms of plagiarism, including paraphrasing and citation manipulation. AI-based plagiarism detection systems, which leverage machine learning algorithms to analyze the structure and semantics of student work, have shown great potential in providing more accurate and efficient plagiarism detection. This study aims to compare the effectiveness of AI-based plagiarism detection tools against traditional methods to understand their respective strengths and weaknesses in the context of Kaduna Polytechnic.
Statement of the Problem
Plagiarism is a significant concern at Kaduna Polytechnic, and the methods currently used to detect it, including manual review and basic plagiarism-checking software, often fail to identify sophisticated forms of academic dishonesty. This limitation is due to the inability of traditional systems to detect subtle paraphrasing or the manipulation of sources. AI-based systems, however, are designed to go beyond keyword matching, using deep learning algorithms to analyze writing style, sentence structure, and even the context of citations. This research aims to explore the efficacy of AI-based plagiarism detection tools and compare them to traditional methods.
Objectives of the Study
1. To compare the effectiveness of AI-based plagiarism detection systems and traditional plagiarism detection methods at Kaduna Polytechnic.
2. To evaluate the accuracy of AI-based plagiarism detection tools in identifying different types of plagiarism.
3. To assess the usability and feasibility of implementing AI-based plagiarism detection systems in academic settings.
Research Questions
1. How does the accuracy of AI-based plagiarism detection compare to traditional methods in identifying plagiarism in student submissions at Kaduna Polytechnic?
2. What types of plagiarism can AI-based systems detect that traditional methods cannot?
3. What is the perception of students and faculty regarding the use of AI-based plagiarism detection systems?
Research Hypotheses
1. AI-based plagiarism detection systems are more accurate than traditional methods in detecting both common and sophisticated plagiarism.
2. The AI-based system can identify more forms of plagiarism, such as paraphrasing, that traditional methods fail to detect.
3. Both students and faculty are more likely to support the use of AI-based plagiarism detection systems over traditional methods.
Significance of the Study
This study will help Kaduna Polytechnic improve its academic integrity protocols by adopting more accurate and efficient AI-based plagiarism detection tools. It will also contribute to the broader academic community by providing a comparative analysis of AI and traditional plagiarism detection methods.
Scope and Limitations of the Study
The study will focus on evaluating plagiarism detection methods for student assignments and research papers at Kaduna Polytechnic. Limitations include potential challenges in implementing AI systems within the existing infrastructure and the need for adequate training for faculty and students in using the new system.
Definitions of Terms
• Plagiarism Detection: The process of identifying instances where students copy or improperly reference work that is not their own.
• AI-Based Plagiarism Detection: A system that uses artificial intelligence to identify patterns of plagiarism by analyzing the semantics, structure, and context of student work.
• Traditional Plagiarism Detection: Manual or software-based systems that compare student work to a database of known sources for similarity.
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