Development of an AI-Based Question Paper Generation System in Federal College of Education, Zaria, Kaduna State
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- NGN 5000
Background of the Study
The process of generating question papers for exams in educational institutions is a labor-intensive task that requires careful consideration of various factors such as syllabus coverage, question difficulty, and subject balance. At the Federal College of Education in Zaria, Kaduna State, this process is traditionally done manually, often leading to repetitive patterns, human errors, and inefficiencies. The use of AI for automating question paper generation has the potential to streamline this process by ensuring that questions are diverse, aligned with the curriculum, and vary in difficulty levels. AI can analyze past exam papers, syllabus guidelines, and student performance data to generate question papers that meet academic standards and address any gaps in the learning process. This study explores the development and implementation of an AI-based question paper generation system at the Federal College of Education, Zaria.
Statement of the Problem
At the Federal College of Education, Zaria, the manual generation of question papers often leads to redundancy, imbalanced question distributions, and failure to adhere to the proper difficulty levels. Moreover, the process is time-consuming and prone to errors, affecting both faculty workload and the quality of assessments. AI-based systems offer an opportunity to automate and optimize this process by generating question papers that are diverse, balanced, and relevant to the students’ learning progress.
Objectives of the Study
1. To design and develop an AI-based system for generating exam question papers at the Federal College of Education, Zaria.
2. To evaluate the accuracy and effectiveness of the AI-generated question papers in meeting academic standards.
3. To assess the perceptions of faculty and students regarding the quality and fairness of AI-generated question papers.
Research Questions
1. How effective is the AI-based system in generating diverse and balanced question papers that meet academic standards?
2. How does the use of AI in question paper generation compare to traditional manual methods in terms of time and accuracy?
3. What is the perception of faculty and students regarding the fairness and quality of AI-generated question papers?
Research Hypotheses
1. AI-based question paper generation produces more balanced and diverse question papers compared to traditional methods.
2. The use of AI for question paper generation reduces the time spent by faculty in preparing exam papers.
3. Faculty and students will perceive AI-generated question papers as fair and of high quality.
Significance of the Study
This research will contribute to improving the efficiency and quality of assessment processes at the Federal College of Education, Zaria, by introducing an AI-based solution for question paper generation. It will also provide a model for other institutions looking to automate and enhance their assessment systems.
Scope and Limitations of the Study
The study will focus on the development and evaluation of the AI-based question paper generation system within the Federal College of Education, Zaria. The system will be tested for a selection of courses within the college. Limitations include potential resistance to new technology and challenges in integrating the AI system with existing examination protocols.
Definitions of Terms
• AI-Based Question Paper Generation: The use of artificial intelligence algorithms to automatically generate exam question papers based on predefined criteria such as syllabus coverage, question balance, and difficulty levels.
• Curriculum Alignment: The process of ensuring that exam questions are in line with the course content and learning objectives.
• Difficulty Level: The relative challenge posed by a question, which should be appropriately distributed across an exam to test varying levels of student understanding.
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