Development of an AI-Powered Academic Fraud Detection System: A Case Study of Niger State Polytechnic, Zungeru, Wushishi LGA
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Background of the Study
Academic fraud undermines the integrity of educational institutions, necessitating robust systems to detect and prevent malpractice. At Niger State Polytechnic in Zungeru, Wushishi LGA, the development of an AI‑powered academic fraud detection system is proposed to safeguard academic processes. Traditional fraud detection methods rely on manual verification and periodic audits, which are time‑consuming and often insufficient to identify sophisticated fraudulent activities. The proposed system utilizes artificial intelligence, particularly machine learning and natural language processing, to analyze large volumes of academic data and detect anomalies indicative of fraud, such as plagiarism, certificate forgery, and manipulation of exam results (Chinwe, 2023; Musa, 2024). By automating data analysis and employing predictive analytics, the system can identify patterns and flag suspicious activities in real‑time, allowing for prompt intervention. Integration with existing academic databases ensures comprehensive monitoring of academic records, while secure data protocols protect sensitive information. The system’s continuous learning capability allows it to adapt to emerging fraud techniques, enhancing its long‑term effectiveness. Despite these advantages, challenges such as algorithmic bias, data quality issues, and ensuring user trust in automated decision‑making remain. Pilot implementations in comparable institutions have demonstrated that AI‑powered solutions can significantly reduce instances of academic fraud, thereby preserving institutional credibility. This study aims to evaluate the performance, accuracy, and scalability of the academic fraud detection system at Niger State Polytechnic, providing a framework for its broader application in higher education (Okafor, 2025).
Statement of the Problem
Niger State Polytechnic currently faces persistent challenges in detecting and preventing academic fraud due to reliance on manual and traditional verification methods. These conventional approaches are not only labor‑intensive but also prone to human error, allowing sophisticated fraudulent activities to go undetected. The lack of an automated, data‑driven fraud detection system compromises the integrity of academic credentials and undermines the institution’s reputation. Although an AI‑powered fraud detection system offers a promising alternative by leveraging advanced algorithms to analyze large datasets and identify irregular patterns, its implementation is hindered by issues such as data quality, algorithmic bias, and limited technical expertise among staff. Additionally, concerns regarding the transparency and interpretability of AI‑based decisions may affect stakeholder trust. The current gap between traditional fraud detection methods and the potential of AI‑driven approaches necessitates an in‑depth evaluation of the system’s effectiveness. This study aims to compare the performance of the AI‑powered system against existing methods, identify the technical and operational challenges, and propose strategies to enhance system accuracy and user confidence. Addressing these issues is crucial for protecting academic standards and ensuring that fraudulent activities are detected and mitigated promptly (Musa, 2024).
Objectives of the Study
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To design and implement an AI‑powered academic fraud detection system.
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To evaluate the system’s accuracy and effectiveness in detecting fraudulent activities.
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To propose strategies for overcoming data quality and algorithmic bias challenges.
Research Questions
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How does the AI‑powered system compare to traditional methods in detecting academic fraud?
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What are the main technical challenges affecting the system’s accuracy?
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Which strategies can improve system transparency and user trust?
Significance of the Study
This study is significant as it addresses the critical need for robust academic fraud detection at Niger State Polytechnic through AI‑driven solutions. By enhancing the detection of fraudulent activities, the system aims to uphold academic integrity and protect institutional reputation. The findings will provide valuable insights for administrators and policymakers seeking to implement effective digital safeguards against academic malpractice (Chinwe, 2023).
Scope and Limitations of the Study
This study is limited to the development and evaluation of an AI‑powered academic fraud detection system at Niger State Polytechnic, Zungeru, Wushishi LGA.
Definitions of Terms
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Academic Fraud Detection System: A digital platform that uses AI to identify fraudulent academic activities.
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Machine Learning: A branch of AI that learns from data to improve predictions over time.
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Algorithmic Bias: Systematic errors in AI outcomes caused by biased training data.
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