Evaluation of AI-Based Student Attendance Prediction Systems in Federal University Birnin Kebbi, Kebbi State
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Background of the Study
Accurate attendance monitoring is a crucial factor in assessing student engagement and academic success. At Federal University Birnin Kebbi, Kebbi State, traditional attendance tracking methods are often manual and prone to errors, leading to inaccurate records and delays in identifying attendance issues. Recently, AI-based student attendance prediction systems have emerged as innovative tools that leverage machine learning and data analytics to forecast student attendance patterns. These systems analyze historical attendance data, class schedules, and other relevant factors to predict future attendance trends and identify students who may be at risk of absenteeism (Olu, 2023). By providing real-time predictions, AI systems enable timely interventions, allowing educators to address issues before they affect academic performance. The use of predictive analytics in attendance monitoring not only improves the accuracy of attendance records but also enhances institutional decision-making regarding resource allocation and student support services (Adebayo, 2024). Despite the potential benefits, challenges such as data integration, algorithmic bias, and privacy concerns must be addressed to ensure that these systems are effective and accepted by stakeholders. This study aims to evaluate the effectiveness of AI-based attendance prediction systems in Federal University Birnin Kebbi by comparing their performance with traditional manual methods, thereby providing insights into their reliability, efficiency, and impact on student engagement (Balogun, 2025).
Statement of the Problem
Traditional student attendance monitoring methods at Federal University Birnin Kebbi are fraught with inefficiencies and inaccuracies that hinder timely intervention for students with chronic absenteeism (Olu, 2023). Manual processes lead to delayed data collection and processing, making it difficult for administrators to identify patterns and take corrective actions promptly. Although AI-based attendance prediction systems offer the promise of real-time monitoring and early detection of attendance issues, their adoption is challenged by obstacles such as the integration of data from disparate sources, ensuring the quality of input data, and addressing privacy concerns related to the handling of sensitive student information (Adebayo, 2024). Moreover, stakeholders express concerns about the transparency and fairness of algorithmic predictions, which may not fully account for contextual factors affecting attendance. The absence of robust empirical evaluations comparing AI-based systems with traditional methods further complicates decision-making regarding the adoption of these technologies. This study seeks to bridge this gap by evaluating the accuracy and effectiveness of AI-based attendance prediction systems, identifying key challenges in their implementation, and providing recommendations to optimize their integration within the existing administrative framework at the university (Balogun, 2025).
Objectives of the Study:
- To evaluate the accuracy and efficiency of AI-based attendance prediction systems.
- To compare the performance of AI-based systems with traditional attendance monitoring methods.
- To propose strategies for addressing data integration and privacy challenges.
Research Questions:
- How effective are AI-based systems in predicting student attendance?
- What are the limitations of traditional attendance monitoring methods?
- How can data integration and privacy concerns be mitigated in AI-based systems?
Significance of the Study
This study is significant as it evaluates AI-based student attendance prediction systems, providing insights into improving attendance monitoring and intervention strategies at Federal University Birnin Kebbi. The findings will help administrators enhance student engagement and resource allocation through more accurate and timely attendance data (Olu, 2023).
Scope and Limitations of the Study:
This study is limited to the evaluation of student attendance prediction systems at Federal University Birnin Kebbi, Kebbi State.
Definitions of Terms:
• AI-Based System: A system that utilizes artificial intelligence to analyze and predict outcomes (Adebayo, 2024).
• Attendance Prediction: The process of forecasting future attendance patterns based on historical data (Olu, 2023).
• Predictive Analytics: Techniques used to predict future outcomes using statistical models (Balogun, 2025).
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