A STUDY ON THE IMPLEMENTATION OF AI IN EXAMINATION AND ACADEMIC MODERATION

The educational landscape is undergoing a fundamental transformation as institutions integrate Artificial Intelligence (AI) and Natural Language Processing (NLP) into assessment workflows. Traditional manual processes for question generation and academic moderation are increasingly hindered by human error, inconsistent difficulty levels, and faculty burnout. This study provides a comprehensive analysis of the implementation of AI tools in automating question paper design, enhancing examination proctoring, and refining grading through human-in-the-loop systems. By evaluating a taxonomy of tools ranging from Large Language Models (LLMs) to biometric proctoring systems, this paper identifies significant gains in efficiency-including reports of a 36% decrease in instructor workload-while addressing critical concerns regarding algorithmic bias, data sovereignty, and the black-box nature of automated decision-making. The findings suggest that while AI can revolutionize assessment, success depends on a hybrid model that maintains rigorous human oversight and ethical governance.