Development of an AI-based Plagiarism Detection System: A Case Study of Bayero University, Kano (Gwale LGA, Kano State)
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Background of the Study
Plagiarism has become a significant issue in academic institutions, where students and researchers sometimes engage in unethical practices of copying or reusing existing works without proper acknowledgment. The proliferation of online resources has further exacerbated this issue, making manual detection difficult and inefficient (Chang et al., 2024). AI-based plagiarism detection systems, which use machine learning algorithms and natural language processing (NLP), offer a more accurate and automated solution to this problem by comparing submitted works against a vast database of academic papers, articles, and other online content (Wang & Chen, 2023). At Bayero University, Kano, implementing such a system could enhance the integrity of academic work and reduce the instances of plagiarism in student assignments, research papers, and dissertations.
Statement of the Problem
Bayero University, Kano, like many other educational institutions, faces the challenge of ensuring the integrity of academic submissions. Manual plagiarism checks are often insufficient, time-consuming, and prone to error. Additionally, there is a lack of effective automated solutions to monitor and detect plagiarism across a wide range of academic work. An AI-based plagiarism detection system could address this issue by offering a more reliable, fast, and scalable method to identify plagiarized content.
Objectives of the Study
- To design and implement an AI-based plagiarism detection system for academic submissions at Bayero University, Kano.
- To evaluate the effectiveness of the AI-based system in identifying instances of plagiarism across various types of academic work.
- To assess the perceptions of faculty and students regarding the usability and efficiency of the AI plagiarism detection system.
Research Questions
- How effective is the AI-based plagiarism detection system in identifying plagiarized content in academic papers?
- What are the perceptions of faculty and students regarding the efficiency and accuracy of the system?
- How does the AI-based system compare to traditional plagiarism detection methods in terms of reliability and user experience?
Research Hypotheses
- The AI-based plagiarism detection system will be more accurate and efficient in identifying plagiarism than manual methods.
- Faculty and students will report higher satisfaction with the AI-based plagiarism detection system than with traditional methods.
- The AI-based system will significantly reduce the time required for plagiarism checking in academic submissions.
Significance of the Study
This study aims to improve the academic integrity at Bayero University, Kano by implementing an AI-based plagiarism detection system that ensures accurate, quick, and efficient identification of plagiarized content. The findings will contribute to the body of knowledge on the application of AI in academic integrity and may serve as a model for other institutions looking to combat plagiarism more effectively.
Scope and Limitations of the Study
The study will focus on the design and implementation of an AI-based plagiarism detection system specifically for academic submissions at Bayero University, Kano, particularly within the Gwale LGA. It will not extend to other types of academic work such as non-research papers or external plagiarism detection tasks. The study will be limited to English-language papers.
Definitions of Terms
AI-Based Plagiarism Detection System: A system that uses artificial intelligence and machine learning algorithms to detect and identify plagiarized content in academic papers.
Plagiarism: The act of using someone else's work or ideas without proper attribution.
Natural Language Processing (NLP): A branch of artificial intelligence that focuses on the interaction between computers and human language, enabling machines to process and understand text.
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