Evaluation of AI-Powered Plagiarism Detection Systems in Postgraduate Research at Federal University, Kashere, Gombe State
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- NGN 5000
Background of the Study
Academic integrity is a fundamental aspect of higher education, particularly at the postgraduate level, where research outputs are expected to contribute original knowledge. Plagiarism, the act of using someone else's work without proper attribution, has become a growing concern in academic institutions worldwide. At Federal University, Kashere, Gombe State, postgraduate students often face challenges in ensuring their research work adheres to academic integrity standards. AI-powered plagiarism detection systems have emerged as effective tools in identifying instances of plagiarism by analyzing vast amounts of text and comparing it against extensive databases of academic and online sources (Ogunleye & Isa, 2023). These systems use machine learning algorithms to detect similarities and potential plagiarism in research papers. This study aims to evaluate the effectiveness of AI-powered plagiarism detection systems in improving academic integrity in postgraduate research at Federal University, Kashere.
Statement of the Problem
Despite the growing availability of plagiarism detection tools, many postgraduate students at Federal University, Kashere, struggle with maintaining academic integrity due to limited access to efficient plagiarism-checking systems. The traditional methods of manual checking are time-consuming and prone to errors. The study seeks to evaluate the effectiveness of AI-powered plagiarism detection systems in identifying and preventing plagiarism in postgraduate research, thereby improving the quality of academic work.
Objectives of the Study
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To assess the current state of plagiarism detection practices in postgraduate research at Federal University, Kashere.
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To evaluate the effectiveness of AI-powered plagiarism detection systems in identifying instances of plagiarism.
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To compare the performance of AI-powered plagiarism detection systems with traditional manual plagiarism-checking methods.
Research Questions
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How effective are AI-powered plagiarism detection systems in identifying plagiarism in postgraduate research at Federal University, Kashere?
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How do AI-powered plagiarism detection systems compare to traditional plagiarism detection methods in terms of accuracy and efficiency?
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What impact does the use of AI-powered plagiarism detection systems have on academic integrity and research quality at Federal University, Kashere?
Research Hypotheses
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AI-powered plagiarism detection systems will be more accurate in identifying instances of plagiarism than traditional methods.
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Postgraduate students using AI-powered plagiarism detection systems will demonstrate improved adherence to academic integrity standards.
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The implementation of AI-powered plagiarism detection systems will lead to a reduction in instances of plagiarism in postgraduate research.
Significance of the Study
This study will provide valuable insights into the effectiveness of AI-powered plagiarism detection systems in postgraduate research, contributing to improved academic integrity and research quality at Federal University, Kashere. The findings will also inform the adoption of AI-driven tools in other academic institutions to enhance research standards.
Scope and Limitations of the Study
The study will focus on postgraduate research at Federal University, Kashere, Gombe State. It will evaluate the effectiveness of AI-powered plagiarism detection systems in comparison to traditional methods. Limitations include potential biases in the AI systems and the availability of a comprehensive plagiarism database.
Definitions of Terms
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AI-Powered Plagiarism Detection: A system that uses artificial intelligence to compare research papers against databases of sources to identify instances of plagiarism.
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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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Postgraduate Research: Research conducted by students pursuing advanced degrees, such as Master's or Doctoral degrees.
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