Comparative Study of AI-Based and Traditional Student Grading Systems in Federal University Kashere, Gombe State
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Background of the Study
Accurate and fair student grading is fundamental to educational quality and academic integrity. At Federal University Kashere in Gombe State, the grading process has traditionally relied on manual assessments and subjective evaluations, which can lead to inconsistencies and biases. With the advent of artificial intelligence (AI), automated grading systems have emerged as a promising alternative, offering rapid, objective, and consistent evaluation of student work (Olufemi, 2023). AI-based grading systems utilize natural language processing (NLP) and machine learning algorithms to assess essays, assignments, and exams, comparing student responses against pre-established benchmarks. These systems can analyze a vast array of parameters, including grammar, coherence, content relevance, and style, providing a holistic evaluation that is less susceptible to human error. In contrast, traditional grading systems are often limited by human subjectivity and workload constraints, which may result in delayed feedback and variability in grading standards (Ibrahim, 2024). A comparative study between AI-based and traditional grading methods can reveal the strengths and limitations of each approach, offering insights into how technology can enhance academic evaluation. While AI-based systems promise increased efficiency and consistency, challenges such as algorithm bias, the need for extensive training data, and difficulties in assessing creative or abstract responses remain. This study aims to critically compare the performance of AI-based grading systems with traditional methods, assessing their accuracy, reliability, and impact on student learning outcomes. The findings will provide a basis for recommending improvements in grading practices that ensure fairness and academic excellence (Chinwe, 2025).
Statement of the Problem
The grading process at Federal University Kashere is currently marred by inconsistencies and delays due to the reliance on traditional, manual evaluation methods. These methods are often subjective, leading to significant variability in student grades and a lack of timely feedback, which in turn affects student motivation and academic improvement (Adebola, 2023). Although AI-based grading systems have the potential to automate and standardize the evaluation process, their adoption has been limited by concerns over algorithmic bias, data quality, and the inability to fully capture the nuances of student creativity. The existing manual grading system not only imposes a heavy workload on educators but also creates opportunities for human error, further compromising the reliability of assessment outcomes. Without a robust comparative analysis between AI-based and traditional grading methods, it remains unclear whether automated systems can provide a fairer and more efficient alternative. This study seeks to address these issues by conducting a comparative evaluation of both grading systems, with the aim of determining their respective strengths and weaknesses. The ultimate goal is to develop a grading framework that combines the benefits of automation with the necessary human oversight to ensure that student performance is assessed accurately and fairly. The findings will offer valuable insights into how grading practices can be reformed to better support student learning and academic success.
Objectives of the Study:
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To compare the accuracy and consistency of AI-based grading with traditional grading methods.
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To evaluate the impact of both grading systems on student learning outcomes.
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To develop recommendations for improving the grading process by integrating AI and human oversight.
Research Questions:
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How do AI-based grading systems compare with traditional methods in terms of accuracy?
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What are the impacts of each grading method on student feedback and academic performance?
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What challenges must be addressed to effectively integrate AI into the grading process?
Significance of the Study
This study is significant as it provides a comprehensive comparison of AI-based and traditional student grading systems, offering insights into how modern technology can enhance academic evaluation. The findings will help educators adopt more consistent, fair, and efficient grading practices, ultimately improving student learning outcomes. The research supports the integration of advanced AI methods with human oversight to optimize assessment processes in higher education (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to the evaluation of grading systems at Federal University Kashere, Gombe State, and does not extend to other assessment methods or institutions.
Definitions of Terms:
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AI-Based Grading: Automated evaluation of student work using artificial intelligence.
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Traditional Grading: Manual assessment methods relying on human evaluation.
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Grading Consistency: The uniformity and reliability of assessment outcomes.
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