Optimization of AI-Based Academic Integrity Detection in University Research Papers in Kebbi State University of Science and Technology, Aliero, Kebbi State
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- NGN 5000
Background of the Study
Academic integrity is a fundamental principle in higher education, ensuring that students produce original work, acknowledge sources, and follow ethical research practices. However, instances of academic dishonesty, such as plagiarism, have become increasingly prevalent with the growth of online resources and digital content. Detecting and preventing academic dishonesty in research papers is critical for maintaining the credibility of academic institutions.
Traditional plagiarism detection methods, while helpful, often rely on keyword matching or simple comparison algorithms. These methods may not be sufficient to detect more sophisticated forms of academic dishonesty, such as paraphrasing, idea theft, or data manipulation. Artificial Intelligence (AI) has the potential to offer more advanced and accurate solutions for academic integrity detection. By leveraging machine learning algorithms, AI systems can analyze text patterns, context, and content originality in a more nuanced way. This study aims to optimize AI-based systems for academic integrity detection in research papers at Kebbi State University of Science and Technology, Aliero.
Statement of the Problem
Kebbi State University of Science and Technology, Aliero, faces challenges in effectively detecting academic dishonesty in research papers. Traditional plagiarism detection tools may not be capable of identifying more sophisticated forms of cheating. The implementation of AI-based academic integrity detection systems has the potential to improve the detection process, but there is a need to optimize these systems to ensure accuracy and efficiency. This study will focus on enhancing the performance of AI-driven plagiarism and integrity detection systems in the university's research papers.
Objectives of the Study
1. To design and optimize an AI-based academic integrity detection system for research papers at Kebbi State University of Science and Technology.
2. To evaluate the effectiveness of the AI-based system in detecting plagiarism and other forms of academic dishonesty.
3. To analyze the challenges and benefits of implementing AI-based integrity detection systems in academic institutions.
Research Questions
1. How effective is the AI-based academic integrity detection system in identifying plagiarism and other forms of academic dishonesty in research papers?
2. What impact does the AI-based system have on the academic integrity culture at Kebbi State University of Science and Technology?
3. What challenges exist in optimizing AI-based systems for academic integrity detection in university research papers?
Research Hypotheses
1. The AI-based academic integrity detection system is more effective in detecting plagiarism and academic dishonesty compared to traditional methods.
2. The implementation of the AI-based system improves the detection of sophisticated forms of academic dishonesty, such as paraphrasing and idea theft.
3. The adoption of AI-based integrity detection systems faces challenges related to system accuracy, user acceptance, and integration with existing academic processes.
Significance of the Study
This study will contribute to enhancing academic integrity at Kebbi State University of Science and Technology by providing a more accurate and efficient system for detecting research paper dishonesty. The findings will inform the broader academic community on the potential of AI-driven solutions for combating plagiarism and ensuring ethical academic practices.
Scope and Limitations of the Study
The study will focus on the design, optimization, and implementation of an AI-based academic integrity detection system for research papers at Kebbi State University of Science and Technology, Aliero. Limitations include technical challenges in system optimization, the availability of data for training the system, and resistance from students and faculty in adopting new technologies.
Definitions of Terms
• AI-Based Academic Integrity Detection: The use of AI technologies to identify instances of plagiarism, paraphrasing, or other forms of academic dishonesty in research papers.
• Academic Integrity: The ethical code that governs academic work, ensuring that students and researchers produce original work and acknowledge sources appropriately.
• Plagiarism: The act of copying someone else's work, ideas, or words without proper attribution.
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