Comparative Study of AI-Based and Manual Methods of Tracking Student Extracurricular Activities in University of Abuja, FCT
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Background of the Study
Extracurricular activities are a vital aspect of the holistic development of university students, contributing to personal growth, social skills, and career readiness. At the University of Abuja, FCT, the tracking of student extracurricular participation has traditionally been managed through manual systems, which are often labor-intensive, inconsistent, and prone to error. In contrast, AI-based tracking systems utilize automated data collection and analysis to monitor student involvement in various activities in real time (Ibrahim, 2023). These systems employ machine learning algorithms to analyze data from campus events, online registration forms, and social media platforms, providing a comprehensive overview of student participation. By automating the tracking process, AI-based methods can ensure that records are up-to-date, accurate, and reflective of the diverse range of activities available to students (Olu, 2024). This technology not only streamlines administrative tasks but also offers valuable insights into student engagement trends, which can inform institutional policies and enhance the overall student experience. However, the adoption of AI in this domain raises challenges, including data integration from multiple sources, ensuring the privacy of student information, and addressing potential biases in automated tracking algorithms (Adebayo, 2023). This study aims to compare the effectiveness of AI-based tracking systems with traditional manual methods in monitoring student extracurricular activities at the University of Abuja, assessing their impact on administrative efficiency and student engagement, and proposing recommendations for improving the tracking process (Balogun, 2025).
Statement of the Problem
The University of Abuja currently relies on manual methods for tracking student extracurricular activities, leading to inefficiencies, inaccuracies, and incomplete records. These traditional methods are labor-intensive and subject to human error, which often results in an inadequate understanding of student engagement in extracurricular programs (Ibrahim, 2023). Although AI-based tracking systems promise enhanced accuracy and real-time data processing, their implementation is hindered by challenges such as data integration from various platforms, concerns over data privacy, and the potential for algorithmic bias (Olu, 2024). Moreover, the lack of standardized processes for collecting and analyzing extracurricular data further complicates efforts to create a comprehensive overview of student participation. This gap in reliable data prevents the university from effectively measuring the impact of extracurricular activities on student development and limits the ability to make data-driven decisions regarding resource allocation and program improvement. Consequently, the institution faces difficulties in promoting a well-rounded educational experience that fully supports student growth. This study seeks to address these challenges by evaluating the performance of AI-based tracking systems relative to manual methods, identifying their respective strengths and limitations, and proposing actionable strategies to optimize the process of monitoring extracurricular engagement (Adebayo, 2023; Balogun, 2025).
Objectives of the Study:
• To compare the accuracy and efficiency of AI-based versus manual tracking methods.
• To evaluate the impact of tracking methods on student engagement metrics.
• To recommend improvements for integrating data and ensuring privacy in tracking systems.
Research Questions:
• How do AI-based tracking systems compare with manual methods in accuracy?
• What challenges affect the implementation of AI-based extracurricular tracking?
• How can data integration and privacy be improved?
Significance of the Study
This study is significant as it provides a comparative analysis of AI-based and manual methods for tracking student extracurricular activities at the University of Abuja. The findings will offer critical insights for enhancing administrative efficiency, improving data accuracy, and fostering greater student engagement through informed policy decisions (Ibrahim, 2023).
Scope and Limitations of the Study:
This study is limited to evaluating student extracurricular activity tracking at the University of Abuja, FCT.
Definitions of Terms:
• AI-Based Tracking: The use of artificial intelligence to monitor and record data automatically (Olu, 2024).
• Extracurricular Activities: Non-academic programs and events that contribute to student development (Ibrahim, 2023).
• Data Integration: The consolidation of data from multiple sources into a unified system (Adebayo, 2023).
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