Development of an AI-Based Scholarship Recommendation System for University Students: A Case Study of Kano State Polytechnic (Fagge LGA, Kano State)
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Background of the Study
Scholarships are crucial for helping university students fund their education, but the process of identifying and applying for suitable scholarships can be overwhelming. With hundreds of scholarships available, students often struggle to find the ones that match their academic achievements, interests, and financial needs. Traditional scholarship allocation systems are often based on general criteria, leading to mismatches between the scholarship opportunities and students' profiles.
Kano State Polytechnic, located in Fagge LGA, Kano State, is home to a diverse student population with varying academic backgrounds and financial needs. The existing scholarship systems at the polytechnic are manual and inefficient, with students often missing out on relevant opportunities due to lack of awareness or improper matching. The advent of artificial intelligence (AI) offers an opportunity to develop a more personalized and efficient scholarship recommendation system. AI algorithms, such as machine learning models, can analyze student profiles and match them with appropriate scholarship programs, improving the overall scholarship distribution process.
Statement of the Problem
The scholarship allocation system at Kano State Polytechnic is inefficient, with students facing difficulties in finding and applying for scholarships that suit their academic and financial needs. The lack of a personalized recommendation system results in many students either applying for irrelevant scholarships or missing out on the ones they qualify for. This problem highlights the need for an AI-based scholarship recommendation system that can automate and optimize the matching process.
Objectives of the Study
1. To design an AI-based scholarship recommendation system for students at Kano State Polytechnic.
2. To evaluate the effectiveness of the AI system in matching students with relevant scholarship opportunities.
3. To assess the impact of the AI-based recommendation system on students' awareness and success in securing scholarships.
Research Questions
1. How can AI algorithms be utilized to develop a personalized scholarship recommendation system for students at Kano State Polytechnic?
2. What factors should be considered when designing the AI-based scholarship recommendation system?
3. How does the AI-based scholarship recommendation system improve the scholarship application process for students?
Research Hypotheses
1. An AI-based scholarship recommendation system will significantly improve the match between students and relevant scholarships at Kano State Polytechnic.
2. The use of AI for scholarship recommendations will lead to higher success rates in scholarship applications among students.
3. Students using the AI-based recommendation system will report greater satisfaction with the scholarship allocation process.
Significance of the Study
This study will contribute to enhancing the scholarship application process at Kano State Polytechnic by providing a personalized, AI-driven recommendation system. The findings will ensure that students are better informed about suitable opportunities, leading to a more efficient and equitable distribution of scholarships.
Scope and Limitations of the Study
The study will focus on the design and evaluation of an AI-based scholarship recommendation system for students at Kano State Polytechnic, located in Fagge LGA, Kano State. The research will be limited to scholarships available to students at this institution and will not include scholarships for other educational levels or institutions.
Definitions of Terms
• Artificial Intelligence (AI): The branch of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence.
• Scholarship Recommendation System: A system that matches students with suitable scholarship opportunities based on their profiles and criteria.
• Machine Learning: A type of AI that allows systems to learn from data and improve their performance over time without being explicitly programmed.
• Personalized Recommendation: A system that tailors its suggestions based on individual preferences, characteristics, and needs.
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