An Evaluation of Artificial Intelligence-Driven Predictive Models for Student Performance: A Case Study of University of Jos, Plateau State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
- Reference Style:
- Recommended for :
- NGN 5000
1.1 Background of the Study
The application of Artificial Intelligence (AI) in educational settings has expanded significantly in recent years, offering innovative approaches to enhance student outcomes. Predictive analytics, powered by AI, has emerged as a powerful tool for forecasting student performance and identifying those at risk of underperforming or dropping out. This is particularly valuable in the context of Nigerian universities, where large student populations and diverse academic backgrounds often make it challenging to provide tailored support for each student (Umar & Ibrahim, 2024).
The University of Jos, Plateau State, a prominent higher education institution in Nigeria, faces challenges related to student retention, performance monitoring, and early intervention. AI-driven predictive models offer the potential to analyze vast amounts of academic data and generate insights that can guide timely interventions, improving student performance and reducing dropout rates. These models use historical academic data, student demographics, and other behavioral indicators to predict future performance and identify at-risk students (Oluwatobi et al., 2025).
This study aims to evaluate the effectiveness and impact of AI-driven predictive models in assessing and improving student performance at the University of Jos. It will explore how AI tools can be integrated into the existing academic framework, their potential to enhance the learning experience, and the challenges involved in their implementation. The research will also examine whether predictive models can provide actionable insights that lead to better academic interventions and personalized support for students.
1.2 Statement of the Problem
The University of Jos, like many other universities in Nigeria, faces challenges in providing personalized academic support for its diverse student population. With an increasing number of students, it becomes difficult to monitor each student's progress effectively and identify those in need of intervention. As a result, many students struggle academically, leading to poor performance and high dropout rates. Traditional methods of academic monitoring are insufficient to address these challenges.
AI-driven predictive models present an opportunity to address these issues by forecasting student performance and identifying at-risk students based on various indicators. However, the implementation of such models at the University of Jos is not without challenges. These include the lack of sufficient historical data, resistance to technology adoption by faculty and administrators, and concerns regarding data privacy. This study aims to evaluate the effectiveness, challenges, and potential benefits of integrating AI-driven predictive models for improving student performance at the University of Jos.
1.3 Objectives of the Study
1. To assess the effectiveness of AI-driven predictive models in forecasting student performance at the University of Jos, Plateau State.
2. To identify the challenges and barriers to implementing AI predictive models in monitoring and improving student performance.
3. To evaluate the potential benefits of using predictive models to personalize academic interventions and support at the University of Jos.
1.4 Research Questions
1. How effective are AI-driven predictive models in forecasting student performance at the University of Jos, Plateau State?
2. What are the challenges and barriers to implementing AI predictive models for student performance assessment at the University of Jos?
3. How can AI-driven predictive models be used to personalize academic interventions and support for students at the University of Jos?
1.5 Research Hypothesis
1. AI-driven predictive models will significantly improve the accuracy of forecasting student performance at the University of Jos.
2. Resistance to technology adoption and insufficient data quality will hinder the effective implementation of AI predictive models at the University of Jos.
3. The use of AI predictive models will lead to more personalized academic interventions, improving overall student performance at the University of Jos.
1.6 Significance of the Study
This study is significant because it provides insights into the application of AI-driven predictive models for enhancing student performance in Nigerian universities. By evaluating the effectiveness of predictive models at the University of Jos, the study will contribute to the growing body of knowledge on the use of AI in higher education and provide a practical framework for implementing similar systems at other Nigerian institutions. Furthermore, the findings will offer valuable information on how predictive analytics can be used to improve student retention, reduce dropout rates, and provide personalized academic support for students, thereby enhancing their learning outcomes.
The study will also be significant to policymakers, educators, and university administrators, offering data-driven evidence on the potential of AI technologies to transform academic management and improve the educational experience for students. Additionally, the research will inform future AI integration in Nigerian higher education systems, offering a roadmap for effective deployment and overcoming challenges related to technology adoption.
1.7 Scope and Limitations of the Study
The scope of this study is focused on the use of AI-driven predictive models for student performance assessment at the University of Jos, Plateau State. The research will evaluate the effectiveness of these models in forecasting student outcomes, identify challenges associated with their implementation, and explore their potential benefits for personalizing academic interventions. However, the study is limited to the experiences and perspectives of the academic staff, students, and administrators at the University of Jos.
Furthermore, the study is constrained by the availability and quality of historical academic data, which may limit the accuracy and effectiveness of the predictive models. The research will also focus on the potential use of predictive models to improve academic interventions, excluding other aspects of student support such as mental health services or extracurricular activities. The findings may not be directly applicable to universities with significantly different administrative structures or technological infrastructure.
1.8 Operational Definition of Terms
1. Artificial Intelligence (AI): The use of computational algorithms and machine learning models to simulate human cognitive functions, such as learning, problem-solving, and decision-making.
2. Predictive Models: AI-driven tools that analyze historical data to forecast future outcomes, such as student performance or academic success.
3. Student Performance: The academic achievements of students, typically measured through grades, test scores, and other academic indicators.
4. Personalized Academic Interventions: Tailored educational strategies designed to address the individual learning needs of students, often based on insights from predictive analytics.
5. Data Privacy: The protection of sensitive personal and academic information from unauthorized access, use, or disclosure, particularly in the context of AI-driven systems.
Related Project Materials
An assessment of nurses’ preparedness in handling intraoperative emergencies in Benue State hospitals.
Background of the Study: Intraoperative emergencies, such as bleeding, cardiac arrest, or respiratory distre...
SIGNIFICANCE OF FAMILY PLANNING IN THE CONTROL OF INCREASING POPULATION IN NIGERIA
Abstract
This study investigated the significance of family planning in the control of increasing population in Nigeria....
A Study on the Impact of Global Economic Trends on Nigeria’s Policy Formulation
Background of the Study
Global economic trends, including shifts in trade patterns, technological advancements, and financial market inte...
An Appraisal of the Effects of Risk Management Training on Employee Performance in Nigerian Firms
Background of the Study
In today’s competitive business landscape, risk management has emerged as a crucial component...
PREVALENCE OF HIV/AIDS AMONG INTENDING DONORS
ABSTRACT
The Human Immune deficiency Virus(HIV) and the Acquired Immune Deficiency Syndrome (AIDS) situation in Nigeria has affected bloo...
An assessment of the role of big data analytics in optimizing marketing strategies: A case study of an FMCG company in Lagos.
Background of the study:
Big data analytics has revolutionized marketing strategies by providing actionable insights derive...
The Effect of IFRS Adoption on Digital Accounting Practices in Nigeria
Background of the Study
The advent of digital technologies has revolutionized accounting practices globally. In Nigeria, th...
An Evaluation of Nurses' Knowledge and Management of Bipolar Disorder in Psychiatric Hospitals in Nigeria
Background of the Study
Bipolar disorder is a severe mental health condition characterized by extreme mood swings, inclu...
MANAGEMENT/LABOUR RELATIONS AND ITS EFFECTS ON PRODUCTIVITY
ABSTRACT
This study is aimed at management-labour relation and its effect on prodcutivity in an organization using NITEL...
THE SOCIAL PROBLEMS AND ACADEMIC PERFORMANCE OF STUDENTS IN SELECTED SECONDARY SCHOOL
ABSTRACT
The study basically examined the relationship between social problems in schools and academic performance o...
