Comparative Study of AI-Based and Traditional University Student Housing Allocation Methods in Ahmadu Bello University, Zaria, Kaduna State
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Background of the Study
University student housing is an essential aspect of university life, as it affects students' overall experience and academic performance. Traditional methods of housing allocation are typically manual and can be inefficient, time-consuming, and prone to human error. These methods often fail to consider important factors such as student preferences, needs, and fairness in distribution. The introduction of artificial intelligence (AI) in housing allocation processes presents an opportunity to automate and optimize the decision-making process.
AI-based systems can analyze a wide array of factors, including academic performance, social needs, accommodation preferences, and availability of rooms, to ensure a fairer and more efficient housing allocation. At Ahmadu Bello University, Zaria, this system could help match students with accommodation options that best suit their individual preferences and needs. The study will compare the effectiveness of AI-based housing allocation systems with traditional methods, assessing efficiency, fairness, and student satisfaction.
Statement of the Problem
Ahmadu Bello University, Zaria currently relies on traditional methods for student housing allocation, which often result in inefficiencies, inequities, and dissatisfaction among students. Students may be placed in accommodation that does not meet their needs or preferences, and the allocation process is often slow and manual. This study aims to explore whether an AI-based housing allocation system can address these challenges by automating the process and ensuring a more fair and efficient distribution of housing resources.
Objectives of the Study
- To compare the efficiency and fairness of AI-based housing allocation systems with traditional methods at Ahmadu Bello University, Zaria.
- To assess the impact of AI-based systems on student satisfaction with housing allocation.
- To identify the challenges and benefits of integrating AI into housing allocation processes in Nigerian universities.
Research Questions
- How does the AI-based housing allocation system compare to traditional methods in terms of efficiency?
- What is the impact of AI-based housing allocation on student satisfaction with their accommodations?
- What are the key challenges and benefits of using AI for housing allocation at Ahmadu Bello University, Zaria?
Research Hypotheses
- The AI-based housing allocation system is more efficient in terms of processing time compared to traditional methods.
- Students allocated housing through the AI-based system report higher satisfaction compared to those allocated through traditional methods.
- The implementation of AI-based housing allocation systems at Ahmadu Bello University faces challenges such as infrastructure limitations and resistance to change.
Significance of the Study
This study will contribute to improving the housing allocation process at Ahmadu Bello University, Zaria by offering a more efficient and equitable solution through AI. It will also provide valuable insights for other Nigerian universities interested in adopting AI to optimize their housing allocation systems.
Scope and Limitations of the Study
The study will focus on the comparative evaluation of AI-based and traditional housing allocation methods at Ahmadu Bello University, Zaria, Kaduna State. The research will be limited to undergraduate students and will not include postgraduate students or off-campus housing allocations.
Definitions of Terms
AI-Based Housing Allocation: The use of artificial intelligence algorithms to automate and optimize the allocation of university housing based on various student and accommodation factors.
Housing Allocation: The process by which students are assigned university accommodation, considering preferences, needs, and availability.
Machine Learning: A branch of AI that allows a system to learn from data and improve its performance without explicit programming.
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