An Investigation of AI-Powered Grading Systems for Objective Assessments in University of Jos, Plateau State
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- NGN 5000
Background of the study
The grading system in universities plays a vital role in assessing student performance and providing feedback on academic achievements. However, traditional grading systems are often prone to human errors, inconsistencies, and biases, which can affect the fairness and accuracy of assessment outcomes. At the University of Jos in Plateau State, there is a growing interest in exploring AI-powered grading systems that can automate the grading process for objective assessments such as multiple-choice questions, true/false tests, and short-answer questions. AI-powered grading systems leverage machine learning algorithms to accurately grade assessments, providing faster feedback to students and reducing the workload of instructors. Additionally, such systems can ensure greater consistency and fairness in grading. This research aims to investigate the potential of AI-powered grading systems for objective assessments at the University of Jos, focusing on their accuracy, efficiency, and ability to enhance the academic assessment process.
Statement of the problem
At the University of Jos, the traditional grading system faces several challenges, particularly in grading objective assessments. Manual grading is time-consuming, prone to errors, and may introduce biases due to human judgment. Additionally, instructors may experience fatigue when grading large numbers of assessments, leading to inconsistencies. The need for a more efficient and accurate grading system is evident. AI-powered grading systems offer the potential to automate the grading process, providing quick and reliable results while reducing the administrative burden on instructors. However, the integration of AI in grading at the University of Jos presents challenges, including the adaptation of faculty and students to the new system, and ensuring the system’s accuracy and fairness.
Objectives of the study
1. To design and implement an AI-powered grading system for objective assessments at the University of Jos.
2. To evaluate the effectiveness and accuracy of the AI system in grading multiple-choice and short-answer assessments.
3. To assess the impact of AI-powered grading systems on the efficiency and fairness of the academic assessment process.
Research questions
1. How accurate and efficient is the AI-powered grading system in evaluating objective assessments at the University of Jos?
2. What are the benefits of using AI-powered grading systems in terms of reducing human errors and biases in grading?
3. How does the implementation of AI-powered grading systems impact the overall assessment process for both students and instructors?
Research hypotheses
1. The implementation of an AI-powered grading system will significantly improve the accuracy and efficiency of grading objective assessments.
2. AI-powered grading systems will reduce human errors and biases in grading compared to traditional methods.
3. The introduction of AI-powered grading systems will result in faster feedback and improved satisfaction among students and instructors.
Significance of the study
This research will contribute valuable knowledge on the application of AI in academic assessment, particularly in objective grading. The findings will help the University of Jos streamline its grading process, reduce errors, and enhance the fairness and efficiency of student evaluations. This study could also serve as a model for other universities in Nigeria and beyond looking to integrate AI in their grading systems.
Scope and limitations of the study
The study is focused on the design and implementation of an AI-powered grading system for objective assessments at the University of Jos, Plateau State. The research will primarily involve the evaluation of AI technologies such as machine learning algorithms for grading multiple-choice questions and short-answer assessments. Limitations include challenges related to system integration with existing infrastructure and resistance to change among faculty members.
Definitions of terms
• Artificial Intelligence (AI): The use of machine learning, natural language processing, and other AI technologies to simulate human intelligence and automate tasks.
• Grading System: The method through which student assessments are evaluated and scores are assigned based on performance.
• Machine Learning (ML): A subset of AI that allows machines to learn from data and improve their performance over time without explicit programming.
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