AI-Powered Quiz Generation and Feedback System

Abstract - Transforming the scenario of educational assessment, automatic quiz generation has made it possible to generate questions automatically and also personalized feedback. Existing systems suffer in content validation and adaptive feedback related to ill-structured questions or even irrelevant questions as these systems cannot be adaptive enough to learning. This research paper discusses an AI-based quiz generation algorithm technique employing a Django tutor system for providing high-quality assessments based on multi-tiered validation and intelligent mechanisms of feedback. The system takes uploaded PDFs as corpus, verifies their content value with keyword-based validation and AI-driven validation, and generates multiple-choice questions from that PDF. The tutors can then review, edit, and deploy quizzes for learners, who try out the same with a timer. AI-powered feedback is used to explain wrong answers to the learner so that learning is reinforced. The framework improves the reliability in assessment, helps create quizzes efficiently, and personalizes the learner's experience. This solution scales out for cloud deployment, while evaluation metrics of question relevance, validation accuracy, and engagement demonstrate improvement. It is possible for the educator to develop such lean assessments, thus enhancing learning outcomes in digital classrooms. The AI-driven feedback mechanism extends beyond automated answer validation and integrates contextual explanations related to wrong answers, as well as offers personalized instruction, thus aiding the learner to figure out the concept of the matter instead of simply memorizing an answer.