Implementation of an AI‑Based Student Course Advisor System: A Case Study of Kwara State University, Malete, Moro LGA
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Background of the Study
Selecting the right courses is crucial for students’ academic and professional success, yet many face difficulties in making informed decisions due to the complexity of course offerings. At Kwara State University, Malete, Moro LGA, the implementation of an AI‑based student course advisor system is proposed to provide personalized guidance by leveraging artificial intelligence. Traditional academic advising relies on manual counseling, which is often limited by time constraints and subjective judgment. The proposed system employs machine learning algorithms and natural language processing to analyze student academic records, interests, and career aspirations. It then generates tailored course recommendations that align with individual strengths and future goals (Adeyemi, 2023; Chinwe, 2024). This digital advisor integrates with the institution’s academic database to offer real‑time recommendations and feedback, thereby enhancing decision‑making processes. Features such as interactive dashboards, automated scheduling of counseling sessions, and data‑driven insights empower students to plan their academic trajectory more effectively. The system also continuously learns from user feedback to refine its recommendations over time. However, challenges such as data privacy, algorithmic bias, and integration with existing academic systems need to be addressed to ensure accuracy and reliability. Pilot studies in similar educational settings have shown that AI‑driven course advisory systems can significantly improve student satisfaction and academic outcomes. This study aims to evaluate the design, implementation, and performance of the AI‑based course advisor system at Kwara State University, providing a comprehensive framework for enhancing academic advising through technology (Okafor, 2024).
Statement of the Problem
Kwara State University currently employs traditional course advising methods that are often inadequate in addressing the diverse needs of students. Manual counseling sessions are time‑consuming and may not provide personalized recommendations, resulting in suboptimal course selections that affect academic performance. Although an AI‑based course advisor system offers the potential for tailored guidance through data-driven analysis, its implementation faces significant challenges. Key issues include ensuring the accuracy of the algorithm’s recommendations, safeguarding sensitive student data, and integrating the system with existing academic databases. Additionally, resistance from both students and staff who are accustomed to conventional advising practices may impede the adoption of the new system. These challenges limit the system’s effectiveness and hinder its ability to improve overall academic outcomes. This study aims to assess the performance of an AI‑based student course advisor system by comparing its recommendations with traditional methods, identifying technical and operational barriers, and proposing strategies to enhance system reliability and user acceptance. Addressing these issues is crucial for creating a sustainable, personalized advising framework that supports students in making informed academic decisions (Chinwe, 2024).
Objectives of the Study
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To design and implement an AI‑based course advisor system that generates personalized recommendations.
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To evaluate the system’s accuracy and impact on student academic planning.
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To propose strategies for ensuring data security and seamless integration.
Research Questions
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How does the AI‑based system improve course recommendation accuracy compared to traditional advising methods?
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What technical challenges affect data integration and algorithm performance?
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Which measures can enhance user acceptance and data privacy?
Significance of the Study
This study is significant as it addresses the need for personalized academic advising at Kwara State University by implementing an AI‑based course advisor system. The system is expected to improve course selection, enhance academic performance, and support informed decision-making, thereby benefiting students and advisors alike. The findings will provide critical insights for integrating AI in academic counseling (Adeyemi, 2023).
Scope and Limitations of the Study
This study is limited to the implementation of an AI‑based student course advisor system at Kwara State University, Malete, Moro LGA.
Definitions of Terms
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Course Advisor System: A digital tool that provides personalized course recommendations.
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Machine Learning: A subset of AI that enables systems to learn from data.
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Algorithmic Bias: Errors in AI outputs due to biased training data.
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