Design of an AI-Based Lecture Attendance Monitoring System in Federal University Kashere, Gombe State
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Background of the Study
Accurate monitoring of lecture attendance is vital for assessing student engagement and ensuring academic accountability. At Federal University Kashere, Gombe State, traditional methods of recording attendance—often relying on manual registers—are inefficient, error-prone, and time-consuming. With the advent of artificial intelligence (AI), there is an opportunity to revolutionize attendance monitoring by designing automated systems that utilize facial recognition, RFID technology, and data analytics to track student attendance in real time (Olu, 2023). AI-based systems can reduce human error and provide instantaneous data on attendance trends, allowing lecturers and administrators to identify patterns of absenteeism and intervene promptly when necessary. These systems are capable of integrating with existing academic management platforms, ensuring that attendance data is automatically updated and easily accessible for analysis (Adebayo, 2024). Additionally, AI-based monitoring can enhance security by verifying the identity of students, thus minimizing fraudulent attendance practices. Despite these advantages, challenges such as high initial setup costs, technical integration issues, and concerns over data privacy persist. There is also a need for robust algorithms that can accurately process attendance data under varying conditions, such as different lighting and classroom environments. This study aims to design an AI-based lecture attendance monitoring system tailored for Federal University Kashere, evaluating its technical feasibility, accuracy, and overall impact on academic management. The research will compare traditional attendance methods with the proposed AI solution to determine improvements in efficiency, reliability, and user satisfaction (Balogun, 2025).
Statement of the Problem
Federal University Kashere currently faces significant inefficiencies in tracking lecture attendance due to reliance on manual, paper-based systems. These traditional methods are not only labor-intensive but also susceptible to errors and fraudulent practices, leading to inaccurate records that hinder effective academic monitoring (Olu, 2023). The absence of a real-time, automated system has resulted in delays in identifying patterns of absenteeism, which in turn affects timely interventions aimed at improving student engagement. Although AI-based solutions promise to address these issues by automating attendance tracking and providing instant data analytics, their implementation is challenged by high costs, technical integration complexities, and privacy concerns (Adebayo, 2024). The current infrastructure at the university may not fully support the deployment of advanced AI systems, and resistance from both students and staff—due to fears of surveillance and data misuse—further complicates the transition. Moreover, ensuring the accuracy of facial recognition and RFID-based systems in varied classroom conditions remains a technical hurdle. Without a robust solution, the university is unable to effectively monitor attendance, thereby compromising the integrity of academic records and hindering efforts to improve student participation. This study seeks to develop and evaluate an AI-based lecture attendance monitoring system that overcomes these challenges and provides a more accurate, efficient, and secure method of tracking student attendance, ultimately contributing to enhanced academic management (Balogun, 2025).
Objectives of the Study:
- To design and implement an AI-based lecture attendance monitoring system.
- To assess the accuracy, efficiency, and reliability of the system compared to traditional methods.
- To recommend solutions for overcoming integration and privacy challenges.
Research Questions:
- How does an AI-based system improve lecture attendance tracking compared to manual methods?
- What are the technical challenges in deploying such a system?
- How can data privacy and user acceptance be ensured?
Significance of the Study
This study is significant as it explores the development of an AI-based lecture attendance monitoring system that promises to improve attendance accuracy and administrative efficiency at Federal University Kashere. The insights gained will help streamline academic management, enhance student engagement, and support data-driven interventions, contributing to a more effective educational environment (Olu, 2023).
Scope and Limitations of the Study:
This study is limited to the design and evaluation of an AI-based attendance monitoring system at Federal University Kashere, Gombe State.
Definitions of Terms:
• AI-Based System: A technology solution utilizing artificial intelligence for automated processes (Adebayo, 2024).
• Lecture Attendance Monitoring: The process of tracking student presence in academic sessions (Olu, 2023).
• RFID Technology: Radio-frequency identification used to automatically identify and track objects or individuals (Balogun, 2025).
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