Implementation of an AI-Based Exam Marking System for Universities in Maiduguri Metropolitan Council, Borno State
- Project Research
- 1-5 Chapters
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- Table of Content: Available
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- NGN 5000
Background of the Study
The process of marking exams in universities is often time-consuming and prone to human error. As educational institutions in Maiduguri Metropolitan Council, Borno State, continue to embrace digital transformation, the need for efficient, accurate, and scalable methods of exam grading becomes more evident. Artificial intelligence (AI) has the potential to revolutionize exam marking by automating the process, increasing efficiency, and reducing the subjectivity and errors associated with manual grading. AI-based systems can not only mark exams but also provide instant feedback to students, enhancing the overall learning experience.
This study explores the implementation of an AI-based exam marking system at universities in Maiduguri, focusing on its ability to automate the grading of both objective and subjective questions. The AI system utilizes machine learning algorithms to evaluate responses, assess their accuracy, and provide fair and consistent grades. By integrating AI into the exam marking process, the study aims to enhance the reliability and efficiency of grading in universities.
Statement of the Problem
Universities in Maiduguri Metropolitan Council, Borno State, face challenges in efficiently and accurately grading exams, particularly for large cohorts. The traditional manual grading process is time-consuming, subjective, and prone to errors, which can lead to delays in the release of results and undermine the trust in the grading system. There is a need for an automated solution that can not only speed up the grading process but also ensure fairness and consistency. AI-based exam marking systems have the potential to address these challenges by automating the grading process and providing accurate and immediate feedback to students.
Objectives of the Study
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To design and implement an AI-based exam marking system for universities in Maiduguri Metropolitan Council, Borno State.
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To evaluate the effectiveness of the AI-based system in accurately and efficiently marking exams.
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To assess the impact of the AI-based exam marking system on the quality of student feedback and overall exam administration.
Research Questions
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How effective is the AI-based exam marking system in ensuring accuracy and fairness in grading?
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What is the impact of the AI-based exam marking system on the efficiency of exam grading and result processing?
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How does the AI-based exam marking system improve the feedback process for students?
Research Hypotheses
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The AI-based exam marking system improves the accuracy and consistency of exam grading.
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The implementation of the AI-based exam marking system reduces the time required to process exam results.
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The AI-based exam marking system enhances the quality of feedback provided to students on their performance.
Significance of the Study
This study will provide valuable insights into the use of AI for exam marking and its potential to improve the efficiency, accuracy, and fairness of grading in universities. The findings will be particularly useful for university administrators in Maiduguri, Borno State, seeking to modernize their assessment processes. Additionally, the research will contribute to the growing body of knowledge on AI applications in education.
Scope and Limitations of the Study
The study will focus on the design and implementation of an AI-based exam marking system in universities in Maiduguri Metropolitan Council, Borno State. Limitations may include technical challenges in developing and implementing the AI system, as well as resistance from stakeholders unfamiliar with AI-based grading.
Definitions of Terms
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AI-Based Exam Marking System: A system that uses artificial intelligence algorithms to automate the grading of exam papers, both objective and subjective, providing instant results and feedback.
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Machine Learning: A branch of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed.
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Feedback: Information provided to students about their performance, which can be used to enhance their learning process.
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