Implementation of an AI‑Based Academic Performance Evaluation System: A Case Study of Kogi State University, Anyigba, Dekina LGA
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Background of the Study
Assessing academic performance accurately is vital for ensuring quality education and student success. At Kogi State University, Anyigba, Dekina LGA, the implementation of an AI‑based academic performance evaluation system is being explored to enhance traditional assessment methods. Conventional evaluation approaches, which rely on manual grading and static metrics, often fail to capture the dynamic aspects of student learning and performance. The AI‑based system utilizes machine learning algorithms and data analytics to analyze multiple dimensions of student performance, including exam scores, participation, assignment submissions, and attendance patterns (Okafor, 2023; Adeyemi, 2024). By processing vast amounts of academic data, the system can identify performance trends and provide personalized insights for each student, enabling timely interventions and tailored support. The system integrates seamlessly with existing academic databases, offering real‑time monitoring and automated reporting that enhance transparency and accountability in the evaluation process. Advanced features such as predictive analytics and natural language processing facilitate the identification of at‑risk students and highlight areas requiring improvement. This transformation of the evaluation process not only reduces administrative workload but also improves the accuracy and objectivity of performance assessments. However, the implementation of AI‑based systems presents challenges including data quality issues, algorithmic bias, and the need for ongoing system training to adapt to changing academic standards. Pilot studies in comparable institutions have indicated that such systems can significantly enhance evaluation accuracy and student outcomes. This study aims to design, implement, and evaluate an AI‑based academic performance evaluation system at Kogi State University, focusing on its technical efficacy, user acceptance, and potential to support evidence‑based academic interventions (Chinwe, 2025).
Statement of the Problem
Kogi State University currently relies on traditional academic performance evaluation methods that are labor‑intensive, subjective, and insufficient in providing real‑time insights into student progress. Manual grading and static assessment criteria often fail to capture the complexities of student learning, resulting in delayed interventions and inconsistent feedback. Although an AI‑based academic performance evaluation system promises to deliver accurate, objective, and timely assessments by analyzing a wide range of performance indicators, its implementation faces several challenges. Key issues include ensuring the integrity and completeness of academic data, mitigating potential biases in AI algorithms, and integrating the system with existing institutional databases. Moreover, there is concern among faculty regarding the transparency and interpretability of AI‑generated evaluations, which may affect trust and acceptance. Resistance from stakeholders accustomed to conventional evaluation methods further complicates the transition to a digital system. This study seeks to address these challenges by systematically evaluating the performance of an AI‑based evaluation system and identifying critical factors that impact its accuracy and reliability. By comparing traditional evaluation outcomes with those produced by the AI‑driven system, the research will identify gaps and propose strategies to enhance data quality, algorithm fairness, and overall system usability. The ultimate goal is to develop a robust, scalable, and trustworthy academic performance evaluation framework that supports proactive student support and informed decision‑making at Kogi State University (Adeyemi, 2024).
Objectives of the Study
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To design and implement an AI‑based academic performance evaluation system.
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To assess the system’s accuracy, objectivity, and timeliness in evaluating student performance.
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To propose strategies for improving data integration and mitigating algorithmic bias.
Research Questions
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How does the AI‑based evaluation system improve performance assessments compared to traditional methods?
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What are the major challenges in data integration and algorithmic bias?
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Which strategies can enhance system transparency and stakeholder acceptance?
Significance of the Study
This study is significant as it examines the potential of AI‑driven evaluation systems to transform academic performance assessment at Kogi State University. By providing timely and objective feedback, the system aims to support proactive student interventions and improve overall academic outcomes. The findings will offer valuable insights for educators and administrators seeking to adopt advanced digital evaluation tools (Okafor, 2023).
Scope and Limitations of the Study
This study is limited to the implementation of an AI‑based academic performance evaluation system at Kogi State University, Anyigba, Dekina LGA.
Definitions of Terms
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Academic Performance Evaluation System: A digital system for assessing student achievement using various performance metrics.
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Machine Learning: A subset of AI that enables systems to learn from data and improve over time.
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Predictive Analytics: Techniques that forecast future outcomes based on historical data.
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