Optimization of AI-Based Lecture Scheduling Systems for Large University Classes in Taraba State University, Jalingo, Taraba State
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- NGN 5000
Background of the Study
Lecture scheduling is a crucial component of university administration, as it directly affects the learning experience and resource utilization (Kang et al., 2023). For large classes, manual scheduling can be particularly challenging due to the complexity of balancing available classrooms, lecturers’ schedules, and student preferences. Traditional scheduling systems often lead to conflicts, underutilization of resources, and overcrowding (Lopez et al., 2024). AI-based scheduling systems can optimize these processes by using machine learning algorithms to predict the most efficient allocation of resources based on historical data and real-time inputs.
At Taraba State University, Jalingo, Taraba State, an AI-powered lecture scheduling system can address the challenges of managing large university classes. The system can automate scheduling, minimize conflicts, and enhance the overall teaching experience by considering factors such as room capacity, lecturer availability, and student preferences. This study will explore the design and implementation of such a system to optimize lecture scheduling for large university classes.
Statement of the Problem
Taraba State University faces challenges in scheduling lectures for large classes, including room allocation conflicts, inefficient use of resources, and difficulties accommodating all student schedules. These issues often lead to dissatisfaction among students and faculty members, as well as logistical challenges for university administrators. Traditional scheduling methods are no longer effective for handling the complexities of large class sizes. AI-based scheduling systems can offer a more efficient, dynamic solution to these problems.
Objectives of the Study
- To design and implement an AI-based lecture scheduling system for large classes at Taraba State University.
- To evaluate the effectiveness of the AI-based system in optimizing lecture scheduling and resource allocation.
- To assess user satisfaction (students and faculty) with the AI-based lecture scheduling system.
Research Questions
- How effective is the AI-based lecture scheduling system in minimizing scheduling conflicts and optimizing resource utilization?
- What are the perceptions of students and faculty regarding the usability and effectiveness of the AI scheduling system?
- How does the AI-based scheduling system compare to traditional methods in terms of efficiency and user satisfaction?
Research Hypotheses
- The AI-based lecture scheduling system will significantly reduce scheduling conflicts and improve resource allocation.
- Faculty and students will report higher levels of satisfaction with the AI-based system compared to traditional scheduling methods.
- The AI system will enhance the overall efficiency of scheduling and reduce administrative burdens.
Significance of the Study
The findings of this study will provide insights into how AI can optimize scheduling systems in universities, particularly in large classes. By streamlining scheduling processes, Taraba State University can improve resource management and enhance the academic experience for both students and faculty.
Scope and Limitations of the Study
This study will focus on the design, implementation, and evaluation of the AI-based lecture scheduling system for large classes at Taraba State University, Jalingo. The study will not cover scheduling for smaller classes or other university departments outside of the academic scheduling process.
Definitions of Terms
AI-Based Lecture Scheduling System: A system powered by artificial intelligence that automates and optimizes the scheduling of university lectures.
Resource Allocation: The process of assigning available resources, such as classrooms and lecturers, to scheduled activities.
Scheduling Conflicts: Situations where multiple activities overlap, leading to the unavailability of resources such as classrooms or lecturers.
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