Comparative Study of AI-Based and Traditional Methods for University Student Disciplinary Case Management: A Case Study of Benue State University, Makurdi (Makurdi LGA, Benue State)
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- NGN 5000
Background of the Study
Managing student disciplinary cases is a critical function for universities, as it ensures that students uphold academic integrity, respect university policies, and contribute to a conducive learning environment. Traditional methods of managing disciplinary cases, such as manual record-keeping, hearings, and faculty involvement, can be time-consuming, inconsistent, and susceptible to human bias (Bello et al., 2024). AI-based systems, on the other hand, use data analytics and machine learning algorithms to automate and streamline disciplinary case management. These systems can analyze past cases, identify patterns, and make recommendations based on pre-established rules, improving decision-making and ensuring fairer, more efficient processes (Akintoye & Nwankwo, 2025).
Benue State University, Makurdi, located in Makurdi LGA, Benue State, presents an ideal case study for comparing AI-based and traditional methods of student disciplinary case management. The university has a significant student population, which results in a high number of disciplinary cases. This study aims to evaluate the effectiveness of AI-based systems in managing these cases compared to traditional methods, focusing on efficiency, fairness, and outcomes.
Statement of the Problem
At Benue State University, the traditional approach to managing student disciplinary cases is resource-intensive and prone to inconsistencies. The manual review of cases, coupled with a lack of standardized decision-making processes, often leads to delays, inefficiencies, and perceptions of unfair treatment among students. AI-based systems offer the potential to address these challenges by automating case reviews, ensuring consistent decision-making, and improving overall case management. However, the comparative effectiveness of AI-based and traditional methods in this context remains largely unexplored.
Objectives of the Study
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To compare the effectiveness of AI-based systems with traditional methods in managing student disciplinary cases at Benue State University, Makurdi.
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To evaluate the impact of AI-based disciplinary case management on the efficiency and fairness of decision-making.
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To assess student and staff perceptions of AI-based versus traditional disciplinary systems.
Research Questions
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How do AI-based and traditional methods of student disciplinary case management compare in terms of effectiveness at Benue State University, Makurdi?
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What is the impact of AI-based disciplinary case management on the efficiency and fairness of decision-making?
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How do students and staff perceive the AI-based and traditional methods of handling disciplinary cases?
Significance of the Study
This study will contribute to the understanding of how AI can transform student disciplinary case management in universities. The findings could lead to the adoption of AI-based systems at Benue State University and other institutions, enhancing the efficiency and fairness of disciplinary processes.
Scope and Limitations of the Study
The study will focus on comparing AI-based and traditional methods for managing student disciplinary cases at Benue State University, Makurdi, located in Makurdi LGA, Benue State. The research will examine the effectiveness, efficiency, and fairness of both systems, excluding other aspects of university administration.
Definitions of Terms
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AI-Based Disciplinary Case Management: The use of artificial intelligence systems to automate, analyze, and manage student disciplinary cases based on predefined rules and data analysis.
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Student Disciplinary Cases: Instances where students violate university policies, rules, or ethical standards, requiring investigation and potential penalties.
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Machine Learning: A form of AI that allows systems to learn from data and improve decision-making without being explicitly programmed.
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