An Intelligent AI-Based Proctored Examination Framework with Automated Question Generation and Performance Analytics

Abstract

The current systems of on-line examination which help with competitive examinations have major flaws such as inadequate backup support of databases for questions, lack of categorization of difficulty levels of questions, inefficient general performance measures, and ineffective or incomplete monitoring systems. These flaws undermine the validity of the tests, and hence, make it difficult for them to evaluate candidates’ readiness. This study proposes a model of intelligent and AI-based proctored examinations which includes question management in real-time, question categorization based on difficulty levels, and overall performance assessment.The device consists of AI-driven proctoring techniques to reveal candidate conduct throughout examinations and find out capability malpractices, thereby improving examination integrity. Additionally, superior analytics are hired to generate exact standard performance insights, permitting purpose evaluation of learner development. The proposed framework ambitions to decorate the fairness, reliability, and effectiveness of online competitive examination exams and can be extended to massive-scale examination systems past MPSC education.