Development of an AI‑Powered Academic Integrity Detection System: A Case Study of University of Jos, Jos North LGA
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Background of the Study
Academic integrity is a cornerstone of higher education, yet incidents of plagiarism and exam malpractice continue to undermine educational standards. At University of Jos, Jos North LGA, the development of an AI‑powered academic integrity detection system is proposed to automate the identification of fraudulent academic activities. Traditional methods of detecting academic dishonesty are manual and labor‑intensive, often failing to keep pace with sophisticated cheating techniques. The proposed system utilizes artificial intelligence, including machine learning and natural language processing, to analyze student submissions, exam responses, and research outputs for indicators of misconduct (Chinwe, 2023; Adeyemi, 2024). By processing large volumes of data in real time, the system can flag anomalies such as text similarities, unusual answer patterns, and inconsistencies in data. Integration with institutional databases ensures that the system continuously learns from historical records, improving its detection accuracy over time. The digital solution not only enhances the objectivity and speed of integrity assessments but also serves as a deterrent to potential misconduct. Features such as automated report generation, customizable alert thresholds, and user feedback mechanisms further support administrators in maintaining high academic standards. However, challenges such as data privacy, potential algorithmic bias, and the need for regular system updates remain significant. Pilot studies in comparable institutions have demonstrated the effectiveness of AI-driven integrity detection systems in reducing instances of academic fraud. This study aims to evaluate the design, implementation, and operational performance of the AI‑powered academic integrity detection system at University of Jos, providing a scalable framework for upholding academic standards (Okafor, 2024).
Statement of the Problem
University of Jos currently faces persistent challenges in maintaining academic integrity due to traditional, manual methods of fraud detection that are inefficient and prone to error. These methods fail to identify sophisticated cases of plagiarism and exam malpractice in a timely manner, thereby compromising the credibility of academic assessments. Although an AI‑powered academic integrity detection system offers a promising alternative by automating the analysis of academic content and providing real‑time alerts, its implementation is hindered by several challenges. Key issues include ensuring data privacy, addressing algorithmic bias, and integrating the system with existing academic databases. Additionally, there is skepticism among faculty regarding the reliability of automated systems in capturing the nuances of academic dishonesty. The lack of comprehensive, data‑driven monitoring undermines institutional efforts to enforce ethical standards and affects the overall reputation of the university. This study seeks to evaluate the operational effectiveness of an AI‑powered integrity detection system by comparing its performance with traditional methods, identifying critical technical and operational barriers, and proposing strategies to enhance accuracy and user trust. Addressing these challenges is essential for establishing a robust, scalable system that upholds academic integrity and supports a fair evaluation process (Adeyemi, 2024).
Objectives of the Study
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To design and implement an AI‑powered academic integrity detection system.
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To evaluate the system’s accuracy and operational efficiency in detecting academic fraud.
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To propose strategies for mitigating data privacy issues and algorithmic bias.
Research Questions
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How does the AI‑powered system improve the detection of academic fraud compared to traditional methods?
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What technical challenges affect system integration and data privacy?
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Which measures can enhance the reliability and transparency of the system?
Significance of the Study
This study is significant as it seeks to enhance academic integrity at University of Jos by implementing an AI‑powered system for detecting fraudulent practices. The digital solution is expected to improve the objectivity and efficiency of integrity assessments, thereby safeguarding academic standards and reinforcing institutional credibility. The findings will provide valuable insights for educational policymakers and administrators seeking to deploy advanced integrity detection tools (Chinwe, 2023).
Scope and Limitations of the Study
This study is limited to the development and evaluation of an AI‑powered academic integrity detection system at University of Jos, Jos North LGA.
Definitions of Terms
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Academic Integrity Detection System: A digital platform that uses AI to identify fraudulent academic activities.
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Plagiarism: The act of using someone else’s work without proper attribution.
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Algorithmic Bias: Systematic errors in AI outputs resulting from biased training data.
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