Traditional examination techniques have been completely transformed by the incorporation of machine learning (ML) and artificial intelligence (AI) into educational assessment. A machine learning-driven artificial intelligence examination system is presented in this paper with the goal of improving assessment and evaluation procedures. The system uses deep learning, computer vision, and natural language processing (NLP) to enable automated question creation, proctoring, and grading.Adaptive testing methods are used in the suggested system to tailor the exam experience to each candidate's skill level. To stop cheating, it employs AI-powered proctoring that includes behavior analysis and facial recognition. Furthermore, ML algorithms evaluate both objective and subjective responses, decreasing human bias and boosting productivity. In order to assist educators and institutions in making well-informed decisions regarding student performance, the system offers real-time analytics and feedback.Scalability, dependability, and equity in grading are guaranteed by the automated evaluation framework. Additionally, it reduces administrative effort while preserving the integrity of the assessment. Data security, ethical issues, and bias mitigation are some of the issues and solutions related to AI-based tests that are covered in the study.This system has the potential to revolutionize digital assessments by providing accurate, scalable, and reasonably priced examination solutions for professional certification programs, colleges, and universities thanks to AI advancements. Future research will focus on integrating blockchain for result authentication and improving explainability in AI grading.The use of artificial intelligence (AI) and machine learning (ML) in educational assessment has fundamentally changed traditional examination methods. In order to improve assessment and evaluation processes, this paper presents an artificial intelligence examination system that is driven by machine learning. The system enables automated question creation, proctoring, and grading through the use of deep learning, computer vision, and natural language processing (NLP).The proposed system adapts the exam experience to the skill level of each candidate by using adaptive testing techniques. It uses AI-powered proctoring, which incorporates facial recognition and behavior analysis, to prevent cheating. Additionally, ML algorithms assess both subjective and objective responses, reducing human bias and increasing efficiency.
, Tensorflow or Pytorch, ML, Deep Learning, NLP.
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