Enhancing University Course Selection Systems Using AI-Based Recommendation Models: A Case Study of Ahmadu Bello University, Zaria (Zaria LGA, Kaduna State)
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Background of the Study
The process of selecting university courses is a pivotal aspect of academic success, but it is often a challenging and complex task for students. The traditional course selection methods in universities tend to rely on students’ prior knowledge, recommendations from academic advisors, and manual course catalogs, which can be overwhelming and inefficient (Nelson & Choi, 2024). Course selection plays a vital role in shaping students' academic paths, and poor decision-making in this process can result in academic dissatisfaction, delayed graduation, and the mismatch of students' interests and career goals (Jenkins et al., 2023).
Recent advancements in artificial intelligence (AI) have provided innovative solutions to address this challenge. AI-based recommendation systems, similar to those employed by online platforms like Netflix and Amazon, have the potential to revolutionize the course selection process. By analyzing students' academic history, preferences, and future career goals, AI systems can offer personalized course recommendations that are aligned with students' needs and aspirations (Kumar & Singh, 2025). These AI models use machine learning techniques to adapt and refine their recommendations based on continuous data inputs, offering students a more streamlined, efficient, and tailored approach to selecting courses.
Ahmadu Bello University (ABU) in Zaria, Kaduna State, provides a valuable case study for the implementation of AI-based course selection systems in a university setting. This study will explore how such a system can enhance the course selection experience for ABU students, ensuring that their choices are informed, efficient, and aligned with both academic requirements and career aspirations (Jones et al., 2023).
Statement of the Problem
At Ahmadu Bello University, the current course selection process is manual and can be overwhelming for students due to the complexity of available options, prerequisites, and varying career trajectories. Many students find themselves making poor decisions, such as choosing courses outside their area of interest, taking unnecessary courses, or even missing out on essential courses (Adams & Smith, 2024). As a result, students experience delays in their academic progression, which can lead to frustration and lower academic satisfaction. This study seeks to investigate the role of AI-based recommendation models in streamlining and enhancing the course selection process at Ahmadu Bello University, aiming to optimize academic choices and improve overall student satisfaction.
Objectives of the Study
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To evaluate the effectiveness of AI-based recommendation models in enhancing the course selection process at Ahmadu Bello University.
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To assess the impact of AI-driven course recommendations on student satisfaction and academic success.
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To investigate the feasibility of implementing an AI-based course selection system at Ahmadu Bello University.
Research Questions
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How effective are AI-based recommendation models in assisting students with course selection at Ahmadu Bello University?
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What impact do AI-driven course recommendations have on student satisfaction and academic performance at Ahmadu Bello University?
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What are the potential challenges and benefits of implementing an AI-based course selection system at Ahmadu Bello University?
Research Hypotheses
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AI-based recommendation models will significantly improve the accuracy and efficiency of the course selection process at Ahmadu Bello University.
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AI-driven course recommendations will enhance student satisfaction and academic performance.
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The implementation of AI-based course selection systems at Ahmadu Bello University will result in more informed and personalized academic choices for students.
Significance of the Study
This study will provide insights into the potential benefits of integrating AI-based recommendation systems into university course selection processes. It will contribute to the academic community by offering evidence of how AI technologies can enhance decision-making, improve student satisfaction, and streamline administrative procedures. The findings will be valuable for Ahmadu Bello University and other institutions seeking to improve the student academic experience.
Scope and Limitations of the Study
The study will focus on the course selection process at Ahmadu Bello University, Zaria LGA, Kaduna State, and will evaluate the implementation of AI-based recommendation models. Limitations include the need for substantial data collection from students and faculty, as well as the potential challenges associated with the integration of AI technology within the university's existing systems.
Definitions of Terms
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AI-Based Recommendation Models: Artificial intelligence systems that suggest courses or content based on a user’s preferences, past behavior, and other data inputs.
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Course Selection: The process by which students choose their courses or modules for each academic term based on their interests, academic requirements, and career goals.
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Machine Learning: A subset of AI that uses algorithms to identify patterns in data and make predictions or decisions without being explicitly programmed.
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