Implementation of AI-Based Digital Examination Invigilation Systems in Federal University, Lafia, Nasarawa State
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Background of the study
Examination invigilation has traditionally been a manual process, with human invigilators overseeing the conduct of exams to ensure fairness and prevent cheating. However, as the number of students and institutions increases, the need for more efficient and scalable examination monitoring systems has become apparent. AI-based digital examination invigilation systems have emerged as a promising solution, using machine learning, computer vision, and other AI technologies to monitor students during exams and detect potential cheating behaviors in real-time. In Federal University, Lafia, Nasarawa State, implementing such a system could reduce the dependency on human invigilators, minimize errors, and enhance exam security. This study will focus on the development and implementation of an AI-based digital examination invigilation system designed to monitor student behavior during examinations and ensure integrity.
Statement of the problem
At Federal University, Lafia, the manual process of exam invigilation has been prone to human error, limited capacity to monitor large numbers of students, and the possibility of cheating during exams. The lack of a reliable and scalable digital system to monitor exams leaves the university vulnerable to academic dishonesty. The need for a more advanced and automated system to oversee the examination process and uphold academic integrity has become essential. AI-driven digital invigilation systems present an opportunity to address these challenges, ensuring that exams are conducted fairly while reducing the workload on human invigilators. However, the development and implementation of such systems in Nigerian universities remain underexplored. This study seeks to fill this gap by implementing an AI-based digital examination invigilation system at Federal University, Lafia.
Objectives of the study
1. To design and implement an AI-based digital examination invigilation system at Federal University, Lafia.
2. To evaluate the effectiveness of the AI-based system in monitoring student behavior during examinations.
3. To assess the impact of the AI-based invigilation system on exam security and the prevention of cheating.
Research questions
1. How effective is the AI-based digital examination invigilation system in monitoring students during exams at Federal University, Lafia?
2. What are the potential challenges associated with implementing AI-based invigilation systems in university exams?
3. How does the AI-based invigilation system impact the prevention of academic dishonesty during exams?
Research hypotheses
1. The AI-based digital examination invigilation system will effectively monitor student behavior during exams and detect cheating.
2. The implementation of the AI-based system will lead to a significant reduction in instances of academic dishonesty during exams.
3. Students will perceive the AI-based invigilation system as an effective and non-intrusive means of ensuring fairness during exams.
Significance of the study
This research will contribute to improving the security and integrity of examinations at Federal University, Lafia, by exploring the potential of AI-based digital invigilation systems. The findings can serve as a model for other universities in Nigeria and beyond that seek to implement advanced technologies to combat cheating and enhance the examination process.
Scope and limitations of the study
The study will focus on the development and evaluation of an AI-based digital examination invigilation system at Federal University, Lafia, Nasarawa State. Limitations may include technological constraints, such as the availability of suitable hardware and software infrastructure, and challenges related to student acceptance of AI-based monitoring systems.
Definitions of terms
• AI-Based Digital Examination Invigilation System: A system that uses artificial intelligence, including computer vision and machine learning algorithms, to monitor students during exams and detect cheating behaviors.
• Academic Dishonesty: Any form of cheating or unethical behavior during exams or academic activities.
• Machine Learning: A field of artificial intelligence that enables systems to learn from data and make decisions or predictions without being explicitly programmed.
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