Design of an AI‑Based Student Performance Prediction System: A Case Study of Bayero University, Kano, Kano Municipal LGA
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Background of the Study
Accurate prediction of student performance is essential for proactive academic support and effective resource planning. At Bayero University, Kano, the design of an AI‑based student performance prediction system aims to transform traditional assessment practices by harnessing machine learning techniques to analyze diverse data sets. These data include historical academic records, attendance logs, socio‑demographic factors, and engagement metrics. AI models such as neural networks, decision trees, and regression analysis can identify patterns that traditional statistical methods may overlook (Okafor, 2023; Musa, 2024). By predicting potential academic challenges early, the system allows for timely interventions tailored to individual student needs. The predictive system is designed to integrate seamlessly with the university’s digital infrastructure, providing continuous updates and enabling educators to monitor student progress in real time. This approach supports a more proactive educational strategy by shifting from reactive measures to preventive interventions. Additionally, the system’s ability to process large datasets in real time offers scalability and adaptability in a dynamic academic environment. However, challenges such as data quality, missing values, and potential biases in algorithmic predictions remain critical. Pilot studies at comparable institutions have shown that AI‑based prediction systems can significantly enhance the accuracy of performance forecasts, leading to improved student retention and academic outcomes (Adeyemi, 2025). This study seeks to design, implement, and evaluate a comprehensive performance prediction system at Bayero University, assessing its impact on student support services and academic planning. The research will explore both technical aspects—such as algorithmic accuracy and data integration—and human factors, including user acceptance and interpretability of the model’s outputs, to provide a balanced view of its effectiveness.
Statement of the Problem
Bayero University currently relies on conventional methods of student performance evaluation that are often retrospective and reactive. These traditional approaches hinder timely interventions and contribute to higher dropout rates. Although AI‑based prediction systems offer the potential to forecast academic performance and enable proactive support, their implementation faces significant challenges. Data inconsistencies, incomplete records, and variability in student demographics complicate the accuracy of predictions. Moreover, there is skepticism among faculty regarding the reliability of algorithm‑driven insights, and concerns persist about the transparency and interpretability of the model’s outcomes (Chukwu, 2023). The integration of AI tools with existing academic information systems further presents technical hurdles, including data interoperability and system scalability. This study aims to bridge the gap between the theoretical benefits of predictive analytics and its practical application in the university setting by rigorously evaluating the performance prediction system. Through a comprehensive analysis of historical data and model outputs, the research seeks to identify key factors that affect prediction accuracy and propose strategies for improving data quality and user confidence. The ultimate goal is to develop an effective framework that supports early interventions, enhances student retention, and contributes to overall academic success at Bayero University (Okafor, 2024).
Objectives of the Study
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To design and implement an AI‑based student performance prediction model.
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To evaluate the model’s accuracy and identify data quality issues.
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To recommend strategies for integrating predictive analytics into academic support services.
Research Questions
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How accurately does the prediction model forecast student performance?
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What data challenges affect the model’s reliability?
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Which interventions can be developed based on model insights?
Significance of the Study
This study is significant as it develops an AI‑based system to predict student performance at Bayero University, enabling early intervention and tailored academic support. The research aims to enhance student retention and academic outcomes by providing data‑driven insights into individual learning trajectories. Findings will inform institutional policies and support educators in implementing proactive measures for student success (Musa, 2024).
Scope and Limitations of the Study
This study is limited to designing and evaluating the performance prediction system at Bayero University and does not extend to other educational institutions.
Definitions of Terms
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Predictive Analytics: Techniques that forecast future outcomes using historical data and machine learning.
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Student Performance: Measures of academic achievement and progress.
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Early Intervention: Proactive support measures implemented before academic failure occurs.
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