Implementation of AI-Based Smart Examination Supervision Systems in Ibrahim Badamasi Babangida University, Lapai, Niger State
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Background of the study
Examination supervision in universities often faces challenges, including cheating, impersonation, and improper invigilation. Traditional methods of examination supervision can be resource-intensive and prone to human error, affecting the overall integrity of the academic assessment process. As educational institutions continue to integrate digital technologies, AI-based solutions offer promising alternatives for addressing these challenges. AI can be used to monitor student behavior during exams, detect unusual patterns or potential cheating, and ensure a more efficient and effective supervision process. The implementation of AI-based smart examination supervision systems in Ibrahim Badamasi Babangida University, Lapai, Niger State, could provide a more secure and reliable method of examination invigilation, ensuring fair and accurate assessments. This study explores the feasibility, design, and effectiveness of AI in enhancing examination supervision at the university.
Statement of the problem
Ibrahim Badamasi Babangida University, Lapai, faces issues related to cheating and improper invigilation during examinations. Traditional methods of examination supervision are often insufficient to identify and prevent dishonest behaviors, leading to unfair academic assessments. The potential for AI-based systems to offer automated and real-time monitoring of exams remains unexplored within the institution. An AI-powered smart examination supervision system could help mitigate academic misconduct by providing efficient monitoring, detecting anomalies, and ensuring adherence to exam protocols. However, the practicality of implementing such a system and its impact on the examination process has not been thoroughly studied.
Objectives of the study
1. To design and implement an AI-based smart examination supervision system for Ibrahim Badamasi Babangida University, Lapai.
2. To evaluate the effectiveness of the AI system in detecting academic misconduct during exams.
3. To assess the feasibility of integrating AI-based invigilation systems into the university's existing examination infrastructure.
Research questions
1. How effective is the AI-based examination supervision system in detecting academic misconduct during exams at Ibrahim Badamasi Babangida University, Lapai?
2. What are the key features of an AI system that contribute to its success in invigilation?
3. How feasible is it to implement an AI-powered exam supervision system in the existing examination setup at the university?
Research hypotheses
1. The AI-based examination supervision system will significantly improve the detection of academic misconduct compared to traditional supervision methods.
2. The AI system will reduce incidents of cheating and impersonation during examinations.
3. The implementation of the AI-powered supervision system will be feasible within the existing examination infrastructure at Ibrahim Badamasi Babangida University, Lapai.
Significance of the study
The findings of this study will contribute to the development of AI-based solutions for improving the integrity of academic assessments. This research will serve as a basis for the adoption of AI technology in university examination processes, potentially enhancing fairness and academic credibility at Ibrahim Badamasi Babangida University, Lapai.
Scope and limitations of the study
The study will focus on the design and implementation of an AI-based smart examination supervision system in Ibrahim Badamasi Babangida University, Lapai, Niger State. The research will be limited to the feasibility of deploying AI within the university's examination system and may face challenges related to system integration and data privacy concerns.
Definitions of terms
• AI-Based Supervision: The use of artificial intelligence algorithms to monitor and supervise exams in real-time, detecting anomalies and potential cheating behaviors.
• Academic Misconduct: Any form of dishonest behavior, such as cheating, plagiarism, or impersonation, during academic assessments.
• Examination Infrastructure: The existing systems, tools, and processes that support the conduct of examinations in an educational institution.
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