Design of an AI-Based Intelligent Student Assignment Grading System in Kaduna Polytechnic, Kaduna State
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- NGN 5000
Background of the Study
The grading of student assignments is a critical component of the academic assessment process, yet traditional methods are often labor-intensive and subject to human error. At Kaduna Polytechnic, Kaduna State, the development of an AI-based intelligent student assignment grading system is being explored as a means to streamline and standardize evaluation processes. This system employs machine learning algorithms and natural language processing to automatically assess the quality of student submissions, providing rapid and objective grading. By analyzing content, structure, and language use, the system can generate scores that reflect both quantitative and qualitative aspects of student work (Chinedu, 2023; Suleiman, 2024). Traditional grading methods, which rely heavily on subjective judgment and manual evaluation, can result in inconsistent scores and delayed feedback. The proposed AI-based system aims to reduce these inconsistencies and deliver prompt, data-driven feedback that can help students improve their performance. Moreover, the integration of such technology supports the broader digital transformation in education, promoting efficiency and transparency in the grading process. However, the implementation of AI grading systems is accompanied by challenges such as ensuring the reliability of the algorithms, addressing potential biases, and safeguarding against over-reliance on automated evaluation. Additionally, concerns about the loss of the human element in assessment and the potential for misinterpretation of nuanced student work must be considered. This study seeks to evaluate the effectiveness of an AI-based grading system at Kaduna Polytechnic by comparing its performance with traditional methods and identifying best practices for its integration. The ultimate goal is to develop a robust grading framework that enhances academic integrity and provides timely, actionable insights into student performance (Amin, 2025).
Statement of the Problem
Kaduna Polytechnic currently faces significant challenges in maintaining consistency and timeliness in the assignment grading process. Traditional manual grading is not only time-consuming but also susceptible to human bias and error, which can compromise the fairness and accuracy of student assessments. Although an AI-based intelligent grading system holds promise for mitigating these issues, its implementation is impeded by several challenges. Key among these are concerns regarding the accuracy and reliability of the grading algorithms, particularly when evaluating creative or subjective responses. Additionally, technical challenges such as system integration, data quality, and the need for continuous algorithm updates pose substantial barriers. There is also apprehension among educators that reliance on automated systems might undermine the nuanced evaluation that human grading provides. Furthermore, data privacy and security issues, as well as potential resistance from faculty, complicate the adoption of AI-based grading. This study aims to address these challenges by rigorously evaluating the performance of an AI-based grading system in real-world scenarios at Kaduna Polytechnic. It will compare the system’s grading outputs with those from traditional methods and assess its impact on grading consistency, efficiency, and overall academic performance. By identifying the strengths and limitations of AI grading, the research intends to offer concrete recommendations to enhance the system’s design and integration, ensuring that it complements rather than replaces the human element in student evaluation (Bello, 2024).
Objectives of the Study
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To evaluate the effectiveness and accuracy of an AI-based assignment grading system.
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To identify technical and operational challenges in implementing the AI grading system.
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To propose strategies for integrating AI-based grading with traditional assessment methods.
Research Questions
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How accurately does the AI grading system assess student assignments compared to traditional methods?
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What technical challenges hinder the effective implementation of AI-based grading at Kaduna Polytechnic?
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Which strategies can improve the integration and reliability of AI grading systems?
Significance of the Study
This study is significant as it examines the potential of AI-based intelligent grading systems to enhance the efficiency, consistency, and objectivity of student assessment at Kaduna Polytechnic. The research will provide insights into best practices for integrating advanced AI tools into academic evaluation processes, ultimately benefiting educators and students by delivering timely and accurate feedback (Nuhu, 2024).
Scope and Limitations of the Study
This study is limited to the design and evaluation of an AI-based intelligent student assignment grading system at Kaduna Polytechnic, Kaduna State.
Definitions of Terms
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Intelligent Grading System: An automated system that uses AI to evaluate and score student assignments.
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Automated Assessment: The use of computer algorithms to perform grading without human intervention.
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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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