Comparative Study of Traditional and AI‐Based Student Performance Evaluation Systems in Kwara State University, Malete, Kwara State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
- Reference Style:
- Recommended for :
- NGN 5000
Background of the Study
In the current era of rapid technological advancement, educational institutions are increasingly exploring innovative methods for evaluating student performance. At Kwara State University, Malete, two distinct evaluation paradigms have emerged: the traditional manual grading system and the newer AI‐based performance evaluation system. Traditional evaluation systems rely heavily on teacher assessment, standardized tests, and manual marking, which, although time‐tested, are often subject to human bias and inconsistency (Adeyemi, 2023). In contrast, AI‐based evaluation systems employ machine learning algorithms, natural language processing, and data analytics to assess student performance in real time. These systems promise improved accuracy, consistency, and efficiency by automatically analyzing large volumes of assessment data and providing instantaneous feedback (Olu, 2024).
The integration of AI into performance evaluation is intended to address several inherent limitations of traditional methods, such as subjective grading and delayed feedback. AI systems are designed to detect patterns in student work, adapt to different learning styles, and reduce the potential for evaluator bias (Balogun, 2025). Additionally, they offer the potential for continuous assessment through data collected from various digital platforms. This holistic approach not only benefits instructors by freeing up valuable time for personalized teaching but also empowers students with immediate insights into their academic progress. Despite these promising developments, the adoption of AI‐based systems also raises important questions regarding their reliability, transparency, and ethical implications. Stakeholders must consider the technological infrastructure, data privacy issues, and the potential devaluation of human judgment in the educational process. This comparative study, therefore, seeks to analyze both systems in terms of cost‐effectiveness, accuracy, user satisfaction, and scalability within the context of Kwara State University, providing evidence‐based recommendations for an optimal evaluation framework (Adeyemi, 2023; Olu, 2024; Balogun, 2025).
Statement of the Problem
Despite the clear advantages offered by AI‐based evaluation systems, Kwara State University continues to rely on traditional assessment methods that are perceived as more familiar and trusted by many educators. This reliance poses significant challenges in achieving consistency and fairness in student evaluations. Traditional systems are not only labor‐intensive but also prone to subjective interpretations that can adversely affect student outcomes. On the other hand, while AI‐based systems offer objectivity and efficiency, there is skepticism regarding their ability to fully understand the nuances of student responses, particularly in subjects that require critical thinking and creativity (Adeyemi, 2023). Furthermore, the integration of advanced technology necessitates significant investment in digital infrastructure and continuous professional development, which may strain institutional resources (Olu, 2024). The lack of clear policy frameworks governing the use of AI in academic assessments further complicates its adoption, leading to resistance among faculty members who fear a loss of professional autonomy. Additionally, data security and ethical considerations surrounding the storage and processing of sensitive student data remain unresolved issues. Consequently, the university is confronted with the dual challenge of maintaining educational integrity while embracing innovative evaluation methods. This study aims to address these concerns by conducting a rigorous comparative analysis of traditional versus AI‐based evaluation systems to determine which method better meets the institution’s goals of fairness, efficiency, and quality in student performance assessment (Balogun, 2025).
Objectives of the Study:
- To compare the accuracy and efficiency of traditional and AI‐based student performance evaluation systems.
- To assess stakeholder perceptions and satisfaction with each evaluation method.
- To recommend an integrated framework that leverages the strengths of both systems.
Research Questions:
- How do traditional and AI‐based evaluation systems compare in terms of accuracy and efficiency?
- What are the perceptions of educators and students regarding the fairness and transparency of each system?
- What framework can best integrate both evaluation methods to optimize student performance assessment?
Significance of the Study
This study is significant as it provides a critical comparative analysis of traditional and AI‐based evaluation systems, offering evidence‐based insights into their strengths and limitations. The findings will inform policymakers and academic administrators at Kwara State University on adopting a balanced approach to student assessment that enhances fairness, reduces bias, and improves operational efficiency. Ultimately, the study aims to foster a more transparent and effective performance evaluation system that benefits both students and educators (Adeyemi, 2023).
Scope and Limitations of the Study:
This study is limited to examining the student performance evaluation systems at Kwara State University, Malete, Kwara State, and does not extend to other academic institutions or evaluation methods.
Definitions of Terms:
- Traditional Evaluation System: A performance assessment approach based on manual grading and teacher judgment (Adeyemi, 2023).
- AI‐Based Evaluation System: A digital assessment method utilizing artificial intelligence and machine learning algorithms (Olu, 2024).
- Student Performance Evaluation: The process of assessing and measuring students’ academic achievements and competencies (Balogun, 2025).
Related Project Materials
THE EFFECTS OF POOR OFFICE ENVIRONMENT ON THE SECRETARY'S JOB PERFORMANCE
ABSTRACT
This research work “The effects of poor office environment on the secretary’s job...
The Impact of Maternal Health Interventions on Infant Mortality Rates in Lagos State, Nigeria
Background of the Study
Maternal health interventions are critical for reducing infant mortality, particul...
EFFECT OF TWITTER BAN ON THE ECONOMY OF NIGERIA
Background Of The Study
The introduction of the internet has revolutionized the way we interact with pe...
An evaluation of sustainability marketing practices on consumer behavior: A study of an eco-friendly product in Port Harcourt, Nigeria
Background of the study
Sustainability marketing emphasizes the promotion of eco-friendly products and responsible consume...
THE ROLE OF FINANCIAL ANALYSIS IN MANAGEMENT CONTROL
EXCERPT FROM THE STUDY
Management control is a process in which organization strives to achieve the planned or desired r...
The Impact of Inventory Management Techniques on Operational Efficiency in Dangote Cement Plc, Kaduna State
Background of the Study
Inventory management is a critical component of supply chain management, particularly in the manufacturing indust...
An assessment of the effectiveness of mangrove restoration in protecting coastal communities in Opobo/Nkoro Local Government Area, Rivers State
Background of the Study
Mangrove restoration is a vital environmental intervention aimed at protecting coastal ecosystems and the communi...
An Investigation of Syntactic Simplification in Nigerian Digital Communication: A Case Study of SMS Language
Background of the Study
The rise of digital communication, particularly through SMS, has led to notable changes in syntact...
EXPLORING THE BENEFITS OF EARLY CHILDHOOD EDUCATION IN SUPPORTING AGING POPULATIONS
ABSTRACT: Exploring the benefits of early childhood education in supportin...
Public Perception of Journalistic Objectivity in Bida LGA, Niger State
Chapter One: Introduction
1.1 Background of the Study
Journalistic objectivity, a core princip...
