Investigation into the Use of AI for Real-Time Student Academic Performance Monitoring in Federal University Gashua, Yobe State
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Background of the Study
Continuous monitoring of student academic performance is crucial for timely interventions and improving learning outcomes. At Federal University Gashua, Yobe State, traditional performance monitoring methods are often retrospective, relying on periodic assessments that do not capture the real-time dynamics of student progress. The advent of AI offers a promising alternative by enabling real-time data collection and analysis through advanced algorithms and learning analytics tools (Olu, 2023). AI-based monitoring systems can process data from online learning platforms, classroom assessments, and digital attendance records to provide immediate feedback on student performance. These systems employ machine learning techniques to identify trends, predict potential academic challenges, and trigger early interventions, thereby enabling educators to address issues before they escalate (Adebayo, 2024). Moreover, real-time monitoring fosters a personalized learning environment where instructional strategies can be dynamically adjusted based on continuous performance data (Balogun, 2025). However, the integration of AI in academic performance monitoring is not without challenges. Key concerns include data privacy, the need for robust data infrastructure, and potential biases in the AI algorithms that may affect the accuracy of predictions. Additionally, there is resistance from stakeholders who are cautious about the implications of automated monitoring on student privacy and academic autonomy. This study seeks to explore the effectiveness of AI-based real-time performance monitoring at Federal University Gashua by comparing it with traditional monitoring methods, evaluating its impact on student outcomes, and proposing recommendations to enhance its implementation (Olu, 2023; Adebayo, 2024; Balogun, 2025).
Statement of the Problem
Federal University Gashua currently faces challenges in accurately monitoring student academic performance due to the limitations of traditional, periodic assessment methods. These methods provide only retrospective insights, delaying intervention and failing to capture real-time fluctuations in student progress (Olu, 2023). As a result, educators often struggle to identify at-risk students promptly, leading to missed opportunities for timely academic support. Although AI-based monitoring systems offer the potential to provide continuous, real-time insights, their adoption is hindered by issues such as data integration, concerns over data privacy, and the reliability of algorithmic predictions (Adebayo, 2024). There is also a notable apprehension among faculty regarding the transparency of AI decision-making processes, which could undermine trust in automated systems. Additionally, the technical infrastructure necessary to support real-time data analytics is often inadequate, further complicating the deployment of AI solutions. These challenges collectively impede the effective implementation of a real-time performance monitoring system, which is essential for proactive academic interventions. This study intends to address these challenges by developing an AI-based monitoring system tailored to the university’s needs, evaluating its performance against traditional methods, and providing recommendations for improving data integration, ensuring privacy, and enhancing algorithmic accuracy (Balogun, 2025).
Objectives of the Study:
• To design an AI-based system for real-time academic performance monitoring.
• To evaluate the system’s effectiveness compared to traditional monitoring methods.
• To recommend strategies for addressing data integration and privacy challenges.
Research Questions:
• How effective is the AI-based system in providing real-time academic performance data?
• What are the key limitations of traditional performance monitoring methods?
• How can data privacy and integration issues be mitigated in AI-based systems?
Significance of the Study
This study is significant as it examines the use of AI for real-time monitoring of student academic performance at Federal University Gashua, aiming to enable timely interventions and improve learning outcomes. The insights gained will inform strategies for deploying advanced analytics in academic environments, thereby enhancing educational support and institutional effectiveness (Olu, 2023).
Scope and Limitations of the Study:
This study is limited to the evaluation of real-time academic performance monitoring at Federal University Gashua, Yobe State.
Definitions of Terms:
• Real-Time Monitoring: The continuous tracking and analysis of performance data as it is generated (Adebayo, 2024).
• Academic Performance: The measurable outcomes of student learning, such as grades and assessment scores (Olu, 2023).
• Learning Analytics: Techniques for analyzing data related to learning processes (Balogun, 2025).
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