Exploring the Role of AI-Based Predictive Analytics in University Research Funding Allocation: A Case Study of Bayero University, Kano (Gwale LGA, Kano State)
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Background of the Study
University research plays a central role in academic advancement, innovation, and societal progress. However, the allocation of research funding is often a complex and subjective process, with decisions based on factors such as institutional priorities, the quality of proposals, and the expertise of researchers (Anderson & Zhao, 2024). Traditional funding allocation methods are frequently slow and prone to inconsistencies, and may not always reflect the evolving needs of the academic community (Garcia et al., 2023).
AI-based predictive analytics has emerged as a potential solution to address these limitations. Predictive analytics uses data-driven models to forecast future outcomes based on historical patterns, enabling better decision-making and more efficient resource allocation (Wang & Lee, 2025). In the context of university research funding, AI can analyze factors such as research proposal quality, historical funding trends, and faculty performance to predict which projects are likely to have the highest impact and deserve funding (Johnson et al., 2023). Bayero University, Kano, offers an ideal case study to explore the potential of AI-based predictive analytics to improve the fairness, transparency, and effectiveness of research funding allocation at Nigerian universities.
Statement of the Problem
At Bayero University, Kano, the process of research funding allocation is often based on subjective criteria, which can lead to inefficiencies, bias, and dissatisfaction among researchers. Researchers sometimes struggle to secure funding, not due to the lack of merit in their proposals, but because of the lack of an objective and data-driven allocation system (Ibrahim & Adamu, 2024). This study will explore the role of AI-based predictive analytics in addressing these challenges, aiming to enhance the transparency, fairness, and efficiency of research funding allocation at Bayero University, Kano.
Objectives of the Study
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To investigate the potential of AI-based predictive analytics in improving research funding allocation at Bayero University, Kano.
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To evaluate the effectiveness of predictive analytics models in identifying high-impact research projects for funding.
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To assess the challenges and benefits of implementing AI-based predictive analytics in research funding allocation at Bayero University, Kano.
Research Questions
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How can AI-based predictive analytics improve research funding allocation at Bayero University, Kano?
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What impact can predictive analytics have on the transparency and fairness of the funding process?
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What are the challenges and benefits associated with implementing AI-based predictive analytics in the research funding process at Bayero University, Kano?
Research Hypotheses
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AI-based predictive analytics will improve the fairness and transparency of the research funding allocation process at Bayero University, Kano.
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AI-based predictive analytics will help identify high-impact research projects more effectively than traditional funding methods.
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The implementation of AI-based predictive analytics will result in more efficient and accurate allocation of research funding at Bayero University, Kano.
Significance of the Study
This study will contribute to the understanding of how AI can enhance the research funding allocation process at universities. It will provide valuable insights for Bayero University and other institutions looking to improve fairness and efficiency in research funding decisions, benefiting both researchers and the academic community at large.
Scope and Limitations of the Study
The study will focus on the research funding allocation process at Bayero University, Kano, located in Gwale LGA, Kano State. Limitations include the availability of data on past research funding decisions and potential resistance to the adoption of AI technology by university administrators.
Definitions of Terms
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AI-Based Predictive Analytics: The use of machine learning and statistical models to predict future outcomes based on historical data.
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Research Funding Allocation: The process by which financial resources are distributed to academic research projects based on various criteria.
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Predictive Modeling: A technique in predictive analytics that uses data to create models that forecast future events or behaviors.
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