Comparative Study of AI-Powered and Manual University Course Allocation Systems in Ibrahim Badamasi Babangida University, Lapai, Niger State
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Background of the Study
University course allocation is a complex process that determines the academic trajectory of students. At Ibrahim Badamasi Babangida University, Lapai, Niger State, course allocation systems have traditionally been managed manually by academic administrators. This manual process, although rooted in personal judgment and experience, often results in inefficiencies, inconsistencies, and delays that may disadvantage students. In contrast, AI-powered course allocation systems utilize advanced algorithms and predictive analytics to match student preferences, academic performance, and course availability in real time (Adeyemi, 2023). These systems are designed to reduce human error and bias, streamline administrative processes, and provide more equitable distribution of academic resources. By integrating data from historical enrollment trends, student feedback, and course performance metrics, AI-powered systems can optimize allocations and adjust recommendations based on dynamic inputs (Olu, 2024). Such automation not only speeds up the allocation process but also improves the overall satisfaction of students by aligning course placements with individual academic goals and institutional needs. Despite these potential benefits, challenges remain regarding the transparency of AI algorithms, the initial costs of implementation, and resistance from staff accustomed to traditional methods (Balogun, 2025). Therefore, a comparative study that critically evaluates both the AI-powered and manual systems is essential to determine their respective strengths and limitations. This research will provide empirical evidence on the efficiency, fairness, and accuracy of each method, offering insights that could lead to an integrated approach combining the benefits of both systems.
Statement of the Problem
Ibrahim Badamasi Babangida University currently relies on a manual course allocation process that is often plagued by inefficiencies and subjective decision-making. Manual systems can lead to delays, errors in allocation, and perceptions of bias, which may affect student satisfaction and academic performance (Adeyemi, 2023). Although AI-powered systems have been proposed as an alternative, there is limited empirical evidence regarding their effectiveness in the university’s context. Concerns about the transparency of AI decision-making and the high initial investment required pose significant barriers to their adoption (Olu, 2024). Moreover, staff members may resist the transition due to apprehensions about relinquishing control over the allocation process and doubts regarding the system’s ability to accommodate unique student needs. The absence of a comprehensive comparison between manual and AI-powered allocation systems creates uncertainty about the best approach to adopt for equitable and efficient course distribution. This study intends to fill that gap by conducting a comparative analysis to determine whether an AI-powered system can outperform traditional methods in terms of speed, accuracy, and fairness. In doing so, it will address concerns related to data integration, algorithmic transparency, and user acceptance, thereby providing a robust framework to guide future implementations and improvements in course allocation processes (Balogun, 2025).
Objectives of the Study:
• To compare the efficiency and accuracy of AI-powered and manual course allocation systems.
• To evaluate user satisfaction and perceptions associated with each system.
• To develop recommendations for integrating AI technologies into the existing allocation framework.
Research Questions:
• How does the performance of AI-powered course allocation compare with the manual system?
• What are the primary concerns of staff and students regarding each method?
• How can an integrated approach improve course allocation outcomes?
Significance of the Study
This study is significant as it provides a comprehensive comparison of AI-powered and manual course allocation systems, offering valuable insights into improving academic planning at Ibrahim Badamasi Babangida University. The findings will assist policymakers in adopting data-driven strategies that enhance fairness, efficiency, and transparency in course allocation, ultimately benefiting both students and administrative staff (Adeyemi, 2023).
Scope and Limitations of the Study:
This study is limited to evaluating course allocation systems at Ibrahim Badamasi Babangida University, Lapai, Niger State.
Definitions of Terms:
• AI-Powered System: A system that utilizes artificial intelligence to automate decision-making processes (Olu, 2024).
• Manual Course Allocation: Traditional methods of course assignment based on human judgment and manual data processing (Adeyemi, 2023).
• Course Allocation: The process of assigning students to academic courses based on various criteria (Balogun, 2025).
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