Design and Implementation of an AI-Based University Admission Recommendation System in Nasarawa State University, Keffi, Nasarawa State
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Background of the study
University admission processes are critical to ensuring that qualified candidates gain access to higher education. However, the traditional admission system at Nasarawa State University (NSUK) in Keffi faces challenges, including inefficient processing, delays in decision-making, and difficulty in evaluating applicants based on multiple criteria. The introduction of an AI-based admission recommendation system has the potential to streamline this process by automating the evaluation of applicants, using algorithms to predict the best-fit candidates for various programs. Such a system could consider academic qualifications, entrance exam results, extracurricular activities, and personal statements to recommend the most suitable candidates. This research aims to design and implement an AI-based recommendation system to optimize the admission process at NSUK, improving decision-making accuracy and efficiency.
Statement of the problem
The current admission system at Nasarawa State University faces several challenges, including slow processing times, human bias in decision-making, and the inability to handle large volumes of applicants effectively. These issues lead to delays in the admission process and may result in less optimal selection of candidates. The reliance on manual processes and subjective criteria can also introduce inconsistencies in the admission decisions. An AI-based university admission recommendation system could significantly enhance the efficiency and objectivity of the admission process, ensuring that only the most suitable candidates are admitted based on predetermined criteria. However, the challenge lies in designing and implementing such a system that aligns with the university's goals and existing infrastructure.
Objectives of the study
1. To design an AI-based admission recommendation system for Nasarawa State University, Keffi.
2. To develop and implement an automated system that evaluates applicants based on multiple criteria such as academic qualifications and entrance exams.
3. To assess the impact of the AI-based recommendation system on the efficiency, fairness, and accuracy of the admission process.
Research questions
1. How can an AI-based recommendation system improve the efficiency of the university admission process at NSUK?
2. What impact does the AI system have on the fairness and accuracy of candidate selection?
3. How does the implementation of an AI-based admission recommendation system affect the overall student experience and university enrollment?
Research hypotheses
1. The implementation of an AI-based admission recommendation system will significantly reduce the time required for processing admission applications.
2. The AI-based system will result in more accurate and fair selection of candidates for admission.
3. The use of the AI system will improve the overall student experience during the admission process.
Significance of the study
This research will provide valuable insights into the application of AI in university admissions, offering a case study for improving admission systems in Nigerian universities. The findings will help Nasarawa State University enhance its admission process, making it more efficient, transparent, and fair, thus improving the institution's reputation and attracting top candidates.
Scope and limitations of the study
This study focuses on the design and implementation of an AI-based admission recommendation system for Nasarawa State University, Keffi. The research will involve the use of machine learning algorithms to assess and recommend candidates based on multiple criteria. The scope is limited to the admission process and does not extend to other administrative functions at NSUK. Limitations include potential challenges in data availability and the need for extensive training of staff and administrators on the new system.
Definitions of terms
• Artificial Intelligence (AI): Technologies that allow computers and machines to perform tasks that traditionally require human intelligence, such as decision-making and problem-solving.
• University Admission System: The process through which universities select and admit students based on various criteria.
• Machine Learning (ML): A subset of AI that involves using algorithms and statistical models to allow machines to improve their performance on tasks through experience and data.
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