Analysis of AI-Based Student Scholarship Eligibility Evaluation Models in Gombe State Polytechnic, Bajoga, Gombe State
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Background of the Study
Scholarship eligibility evaluation traditionally involves manual processes, often relying on a set of fixed criteria such as academic performance, financial need, or extracurricular involvement. However, AI-based models have the potential to make this evaluation process more efficient and accurate. By leveraging data such as past academic performance, socioeconomic status, and other relevant factors, AI can help identify deserving students for scholarships. This study aims to analyze and implement AI-based models for evaluating student scholarship eligibility at Gombe State Polytechnic, Bajoga, Gombe State, providing a fairer and more data-driven approach to scholarship allocation.
Statement of the Problem
The current process of scholarship evaluation at Gombe State Polytechnic relies heavily on manual, static criteria, which can be time-consuming, biased, and inefficient. AI-based models offer the potential to streamline this process, ensuring that scholarship awards are allocated based on a more comprehensive and objective analysis of student data. However, there is limited research on the implementation and effectiveness of AI in the scholarship evaluation process in Nigerian higher education institutions.
Objectives of the Study
1. To develop AI-based student scholarship eligibility evaluation models at Gombe State Polytechnic.
2. To assess the accuracy and fairness of AI-based models compared to traditional scholarship evaluation methods.
3. To evaluate the potential impact of AI-based scholarship models on increasing access to financial support for deserving students.
Research Questions
1. How accurate and fair are AI-based scholarship eligibility models in identifying deserving students?
2. How do AI models compare to traditional scholarship evaluation methods in terms of efficiency and transparency?
3. What is the potential impact of AI-based scholarship evaluation on student access to financial support?
Research Hypotheses
1. AI-based scholarship eligibility models will be more accurate and objective in evaluating students compared to traditional methods.
2. AI models will streamline the scholarship evaluation process and reduce the time required for decision-making.
3. AI-based models will result in a more equitable distribution of scholarship funds, benefiting a broader range of students.
Significance of the Study
This study will contribute to the modernization of scholarship allocation processes at Gombe State Polytechnic, Bajoga, by evaluating the potential benefits of AI. It will provide evidence for the adoption of AI-based systems to ensure more accurate, efficient, and equitable scholarship distribution.
Scope and Limitations of the Study
The study will focus on the analysis of AI-based scholarship eligibility evaluation models at Gombe State Polytechnic, Bajoga. Limitations may include challenges in gathering comprehensive student data for the AI models and the initial adjustment to AI-based decision-making processes.
Definitions of Terms
• AI-Based Model: A machine learning model that uses data to make predictions or decisions without being explicitly programmed.
• Scholarship Eligibility: Criteria that students must meet to qualify for scholarship awards, including academic performance, financial need, and other factors.
• Fairness: The ability of a system to treat all students equitably, without bias or discrimination.
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