Comparative Study of AI-Powered and Traditional Student Assessment Methods in Abubakar Tafawa Balewa University, Bauchi State
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Background of the Study
Assessment is a fundamental component of the educational process, providing essential feedback on student learning and instructional effectiveness. At Abubakar Tafawa Balewa University, Bauchi State, traditional assessment methods typically rely on manual grading and standardized testing, which can be time-consuming and subject to human error. In contrast, AI-powered assessment systems utilize machine learning algorithms and data analytics to automate the evaluation process, providing consistent, rapid, and objective grading of student work (Ibrahim, 2023). These AI systems can analyze diverse forms of student responses, from multiple-choice questions to essays, ensuring a more comprehensive assessment of learning outcomes. Moreover, AI-based assessments offer the advantage of adaptive testing, where the difficulty of subsequent questions adjusts in real time based on a student’s performance, thereby offering a more personalized evaluation experience (Olu, 2024). However, despite these advantages, the integration of AI in student assessments raises concerns regarding the transparency of grading algorithms, potential biases, and the loss of nuanced human judgment. Traditional methods, while slower and less consistent, often benefit from the qualitative insights of experienced educators. Therefore, a comparative study is essential to understand the strengths and limitations of both approaches in the context of Abubakar Tafawa Balewa University. This study aims to evaluate the effectiveness, accuracy, and fairness of AI-powered assessment methods relative to traditional techniques, providing evidence-based recommendations for optimizing assessment practices in higher education (Balogun, 2025).
Statement of the Problem
Abubakar Tafawa Balewa University currently faces challenges in student assessment due to the limitations inherent in traditional evaluation methods, which often rely on manual grading and are susceptible to subjectivity and inconsistency (Ibrahim, 2023). These conventional methods can result in delayed feedback and discrepancies that may affect student performance and academic progression. While AI-powered assessment systems have the potential to deliver more consistent and timely evaluations, their adoption is hindered by concerns related to algorithmic transparency, potential biases in automated grading, and the risk of reducing the qualitative judgment that human assessors provide (Olu, 2024). Additionally, there is uncertainty among educators about the reliability of AI systems, especially in assessing complex responses that require nuanced interpretation. This gap in understanding and acceptance has led to a scenario where the benefits of technological innovation are not fully realized. The lack of comprehensive comparative data between AI-powered and traditional assessment methods further complicates the decision-making process for academic administrators. This study seeks to bridge this gap by systematically comparing the two approaches, thereby identifying their respective advantages and limitations, and offering recommendations for integrating the strengths of AI into the assessment framework without compromising the value of human judgment (Balogun, 2025).
Objectives of the Study:
- To compare the accuracy and efficiency of AI-powered versus traditional assessment methods.
- To evaluate the fairness and reliability of AI-based grading.
- To recommend strategies for integrating AI with human oversight in assessments.
Research Questions:
- How do AI-powered assessment methods compare with traditional methods in terms of accuracy?
- What challenges exist regarding the fairness and transparency of AI-based grading?
- How can AI and traditional assessment methods be integrated for optimal results?
Significance of the Study
This study is significant as it provides a critical comparison of AI-powered and traditional student assessment methods, offering insights that can enhance grading accuracy, fairness, and efficiency. The findings will inform educators and administrators at Abubakar Tafawa Balewa University on how best to integrate innovative technologies with conventional practices to improve overall educational outcomes (Ibrahim, 2023).
Scope and Limitations of the Study:
This study is limited to the evaluation of student assessment methods at Abubakar Tafawa Balewa University, Bauchi State.
Definitions of Terms:
• AI-Powered Assessment: The use of artificial intelligence to automate the evaluation of student work (Olu, 2024).
• Traditional Assessment: Conventional methods of evaluating student performance using manual grading (Ibrahim, 2023).
• Adaptive Testing: Assessment techniques that adjust the difficulty of questions based on student performance (Balogun, 2025).
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