Comparative Study of AI-Based and Traditional University Student Performance Evaluation Systems: A Case Study of Federal University, Lokoja (Lokoja LGA, Kogi State)
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- NGN 5000
Background of the Study
Student performance evaluation has traditionally relied on methods such as exams, assignments, and instructor assessments. While these systems are valuable, they often fail to account for the complexities of student performance and may be subject to bias, inefficiency, and limited scalability (Khan & Singh, 2024). Recent advancements in artificial intelligence (AI) have led to the development of more comprehensive and objective performance evaluation systems that can analyze various types of data, including exam results, participation, assignments, and learning behaviors (Zhang et al., 2025).
Federal University, Lokoja in Kogi State presents an opportunity to explore how AI-based student performance evaluation systems compare to traditional methods in higher education. AI systems can assess students' learning outcomes more holistically, providing real-time feedback and identifying patterns that might go unnoticed by traditional systems (Nguyen & Li, 2023). This study aims to compare the effectiveness, efficiency, and accuracy of AI-based evaluation systems against traditional performance assessment methods at Federal University, Lokoja.
Statement of the Problem
The current student performance evaluation system at Federal University, Lokoja, primarily relies on traditional methods, which may not fully capture the nuances of student learning or provide timely feedback for improvement (Gomez & Wright, 2023). This study will investigate whether AI-based systems can offer a more comprehensive, fair, and efficient alternative to traditional evaluation methods.
Objectives of the Study
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To compare the effectiveness of AI-based and traditional student performance evaluation systems at Federal University, Lokoja.
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To assess the efficiency and accuracy of AI-based systems in evaluating student performance.
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To explore the potential benefits and challenges of integrating AI-based evaluation systems at Federal University, Lokoja.
Research Questions
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How do AI-based student performance evaluation systems compare to traditional methods in terms of accuracy and effectiveness?
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What impact do AI-based evaluation systems have on student performance feedback and academic outcomes?
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What are the benefits and challenges of implementing AI-based evaluation systems in universities?
Research Hypotheses
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AI-based performance evaluation systems will provide more accurate and comprehensive assessments of student performance than traditional methods.
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AI-based evaluation systems will lead to more timely and actionable feedback for students, improving their academic outcomes.
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The implementation of AI-based student evaluation systems will result in greater efficiency and fairness in performance assessments at Federal University, Lokoja.
Significance of the Study
This study will offer insights into the comparative advantages of AI-based evaluation systems, providing Federal University, Lokoja, and other institutions with valuable data on how AI can enhance the student evaluation process. The findings may lead to the wider adoption of AI systems in university performance assessments.
Scope and Limitations of the Study
The study will focus on the evaluation systems used at Federal University, Lokoja, located in Lokoja LGA, Kogi State, and will compare AI-based and traditional methods of assessing student performance. Limitations include the availability of comprehensive student data and the challenges associated with transitioning from traditional to AI-driven systems.
Definitions of Terms
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AI-Based Student Performance Evaluation: The use of artificial intelligence to assess and provide feedback on students' academic performance, based on multiple data sources.
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Traditional Performance Evaluation: Conventional methods of assessing student performance, such as exams, assignments, and instructor assessments.
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Learning Behaviors: The patterns and actions exhibited by students during the learning process, which may include participation, engagement, and study habits.
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