Design of a Machine Learning-Based Academic Fraud Detection System in Federal University Lafia, Nasarawa State
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Background of the Study
Academic fraud undermines the integrity of educational institutions and can have long-lasting effects on the credibility of academic credentials. At Federal University Lafia, Nasarawa State, traditional methods of fraud detection, which rely on manual reviews and sporadic audits, often fail to identify sophisticated fraudulent practices. Machine learning-based systems offer an innovative solution by automating the detection of irregular patterns in academic records, examination scripts, and research outputs (Ibrahim, 2023). These systems utilize algorithms that learn from historical data to recognize anomalies indicative of fraud, such as inconsistencies in grading, plagiarism, and falsification of credentials. By continuously updating and refining their predictive models, machine learning systems can provide real-time alerts, enabling prompt interventions and reducing the risk of academic malpractice (Chinwe, 2024). The integration of such technologies not only enhances the efficiency and accuracy of fraud detection but also promotes a culture of academic integrity. However, challenges such as data quality, algorithmic bias, and the ethical implications of automated surveillance remain critical issues that need to be addressed (Adebayo, 2023). This study aims to design and evaluate a machine learning-based academic fraud detection system tailored for Federal University Lafia, comparing its effectiveness with traditional methods and offering recommendations for improving its performance and ethical compliance (Balogun, 2025).
Statement of the Problem
Federal University Lafia currently relies on conventional fraud detection methods that are often reactive, labor-intensive, and prone to error, thereby compromising academic integrity (Ibrahim, 2023). Manual processes struggle to detect complex and evolving forms of academic fraud, such as collusion, plagiarism, and falsified data, due to their inherent limitations in processing large volumes of information. Although machine learning-based systems hold promise in automating and enhancing fraud detection, their adoption is challenged by issues related to data quality, algorithmic transparency, and potential biases in the detection process (Chinwe, 2024). Moreover, concerns regarding the ethical use of automated surveillance tools and the privacy of academic records further complicate the implementation of such systems (Adebayo, 2023). The absence of a robust, data-driven framework for fraud detection creates vulnerabilities that may undermine the credibility of academic assessments and research outputs. This study seeks to address these issues by developing a machine learning-based system for detecting academic fraud, evaluating its performance against traditional methods, and proposing strategies to improve data integrity, algorithm fairness, and ethical safeguards (Balogun, 2025).
Objectives of the Study:
- To design a machine learning-based system for academic fraud detection.
- To evaluate its performance compared to traditional fraud detection methods.
- To recommend strategies for enhancing data quality, algorithmic fairness, and ethical compliance.
Research Questions:
- How effective is the machine learning-based system in detecting academic fraud?
- What are the limitations of traditional fraud detection methods?
- How can data quality and algorithmic bias be mitigated in the fraud detection process?
Significance of the Study
This study is significant as it investigates the application of machine learning for detecting academic fraud at Federal University Lafia, aiming to enhance the integrity and credibility of academic processes. The findings will provide actionable recommendations to improve fraud detection systems, ensuring a fair and transparent academic environment (Ibrahim, 2023).
Scope and Limitations of the Study:
This study is limited to academic fraud detection at Federal University Lafia, Nasarawa State.
Definitions of Terms:
• Machine Learning: A branch of AI that enables systems to learn from data and improve over time (Chinwe, 2024).
• Academic Fraud Detection: The process of identifying irregularities and dishonest practices in academic records (Ibrahim, 2023).
• Algorithmic Fairness: The principle of ensuring unbiased outcomes in AI systems (Adebayo, 2023).
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