Implementation of AI-Based Course Recommendation for Fresh University Students in Federal University Kashere, Gombe State
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Background of the Study
University course selection is a pivotal decision for new students, impacting their academic trajectory and future career opportunities. At Federal University Kashere, Gombe State, traditional course recommendation methods typically rely on manual academic advising, which can be inconsistent and time-consuming. AI-based course recommendation systems offer a promising alternative by leveraging machine learning algorithms to analyze student profiles, historical academic data, and course content, thereby generating personalized recommendations (Olu, 2023). These systems are designed to streamline the decision-making process by presenting fresh students with tailored course options that match their academic strengths, interests, and career aspirations. The use of AI enables the continuous refinement of recommendations through feedback loops, ensuring that the advice remains relevant as students progress through their studies (Adebayo, 2024). Additionally, AI-driven systems can analyze trends from past enrollment data and adjust recommendations based on evolving academic offerings and market demands, thus enhancing the overall efficiency of the admission process. However, implementing such systems involves challenges including ensuring data accuracy, addressing privacy concerns, and integrating the technology with existing academic databases. The potential for algorithmic bias also raises questions about the fairness of automated recommendations. This study aims to evaluate the effectiveness of an AI-based course recommendation system for fresh university students at Federal University Kashere, comparing its performance with traditional advising methods and proposing strategies to optimize its implementation for better academic and career outcomes (Balogun, 2025).
Statement of the Problem
Federal University Kashere currently employs traditional course recommendation processes that are largely manual and often fail to provide personalized guidance to new students, leading to mismatches between student capabilities and course demands (Olu, 2023). This approach results in suboptimal academic experiences and may hinder students’ future career prospects. Although AI-based course recommendation systems offer a more data-driven and personalized alternative, their implementation faces significant challenges. Key issues include ensuring the accuracy of recommendation algorithms, integrating the system with existing academic records, and addressing concerns over data privacy and potential algorithmic bias (Adebayo, 2024). Moreover, resistance from stakeholders who are accustomed to conventional advising methods further complicates the adoption of AI-driven solutions. Without a reliable and transparent recommendation system, fresh students may not receive the tailored guidance needed to make informed course selections, adversely affecting their academic success. This study seeks to address these challenges by developing and evaluating an AI-based course recommendation system, comparing its outcomes with traditional methods, and recommending strategies to enhance data quality, system integration, and user trust (Balogun, 2025).
Objectives of the Study:
• To design an AI-based course recommendation system for new students.
• To evaluate the system’s performance against traditional advising methods.
• To propose strategies for improving data quality and ensuring transparency in recommendations.
Research Questions:
• How effective is the AI-based recommendation system in guiding course selection?
• What challenges hinder its implementation compared to traditional methods?
• How can issues of data quality and transparency be addressed?
Significance of the Study
This study is significant as it investigates the potential of AI-based course recommendation systems to enhance the academic decision-making process for fresh university students at Federal University Kashere. The findings will provide actionable insights for improving personalized guidance and ensuring equitable course allocation, thereby contributing to better educational outcomes (Olu, 2023).
Scope and Limitations of the Study:
This study is limited to evaluating course recommendation systems for fresh students at Federal University Kashere, Gombe State.
Definitions of Terms:
• AI-Based Course Recommendation: The use of artificial intelligence to suggest academic courses tailored to individual student profiles (Adebayo, 2024).
• Personalized Guidance: Customizing educational advice to meet individual needs (Olu, 2023).
• Algorithmic Bias: The potential for AI systems to produce prejudiced outcomes based on flawed data (Balogun, 2025).
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