Development of an AI-Based University Academic Integrity Monitoring System in University of Abuja, FCT
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Background of the Study
Academic integrity is the cornerstone of higher education, ensuring that scholarship and ethical conduct are maintained. With rapid technological advancements, universities are increasingly challenged by academic misconduct such as plagiarism, cheating, and data fabrication. The University of Abuja, FCT, is developing an AI-based academic integrity monitoring system to address these concerns. This system employs advanced AI techniques—including machine learning and natural language processing—to detect patterns of academic dishonesty in student submissions, research outputs, and examination environments (Kumar, 2023; Singh, 2024). Traditional oversight methods relying on manual reviews are no longer sufficient to manage the volume and sophistication of modern academic fraud. The proposed AI system aims to provide continuous, real-time monitoring by analyzing vast amounts of academic data, thereby identifying anomalies and potential misconduct. By leveraging historical data and adapting to new forms of academic fraud, the system ensures that emerging unethical practices are detected promptly (Harris, 2025). This initiative not only supports academic integrity but also enhances transparency and accountability, fostering a culture of ethical scholarship. The study examines both the technical and ethical aspects of implementing such a system at the University of Abuja. It considers issues related to data privacy, algorithmic bias, and the potential impact on student behavior. The integration of AI into academic monitoring represents a proactive measure to uphold institutional standards while reducing the burden on faculty and administrative staff. As academic institutions strive to safeguard their reputations and maintain rigorous standards, the development of an AI-based monitoring system is a forward-thinking strategy that can serve as a model for other universities facing similar challenges (Garcia, 2023).
Statement of the Problem
Despite the critical importance of academic integrity, the University of Abuja faces significant challenges in effectively monitoring academic misconduct. Traditional oversight mechanisms, which depend on manual review and periodic audits, are insufficient for detecting subtle and sophisticated forms of plagiarism, cheating, and falsification. This limitation is exacerbated by the increasing volume of digital submissions and the diverse methods of academic fraud (Ramirez, 2023). The current system lacks the capacity for continuous, real-time monitoring, resulting in delayed detection and intervention, which can undermine the integrity of academic processes. Although AI-based monitoring systems offer a promising solution, significant concerns remain regarding the accuracy of detection algorithms, potential biases in data analysis, and ethical implications related to constant surveillance (Nguyen, 2024). The absence of a standardized, automated system for monitoring academic integrity has led to inconsistent enforcement of academic policies, thereby eroding trust among stakeholders. Additionally, technical challenges such as data integration, ensuring system interoperability, and protecting sensitive academic records further complicate the deployment of AI solutions. This study seeks to address these issues by developing an AI-based academic integrity monitoring system tailored to the specific needs of the University of Abuja. The research will identify the limitations of current monitoring practices, evaluate the performance of AI algorithms in detecting academic misconduct, and propose strategies to overcome these barriers. The ultimate goal is to create a robust system that enhances the detection of academic dishonesty while supporting a transparent and fair academic environment, thereby strengthening the overall quality of higher education at the institution (Lee, 2025).
Objectives of the Study
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To develop an AI-based academic integrity monitoring system for the University of Abuja.
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To assess the effectiveness and accuracy of AI algorithms in detecting academic misconduct.
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To recommend strategies for integrating AI-based monitoring with existing academic integrity policies.
Research Questions
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How effective is the AI-based system in detecting various forms of academic misconduct?
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What are the primary technical and ethical challenges in implementing AI-based academic integrity monitoring?
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Which strategies can facilitate the integration of AI-based systems with traditional academic oversight mechanisms?
Significance of the Study
This study is significant as it explores the potential of AI-based monitoring systems to uphold academic integrity at the University of Abuja. By automating the detection of academic misconduct, the research aims to enhance the reliability and fairness of academic evaluations, fostering a culture of ethical scholarship. The findings will provide valuable insights for educational policymakers, administrators, and technology developers seeking to implement similar systems in other institutions, ultimately contributing to improved academic standards and institutional reputation (Brown, 2024).
Scope and Limitations of the Study
This study is limited to the development and evaluation of an AI-based academic integrity monitoring system at the University of Abuja and does not cover other aspects of institutional academic policies.
Definitions of Terms
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Academic Integrity: The adherence to ethical principles and standards in academic work, ensuring honesty and fairness.
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AI-Based Monitoring: The use of artificial intelligence technologies to continuously assess and detect irregularities in academic activities.
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Plagiarism: The act of presenting someone else’s work or ideas as one’s own without proper attribution.
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