PEERTUTOR AI: ADAPTIVE PERSONALIZED QUIZ CREATION SYSTEMID: 1862 Abstract :PeerTutor AI: Adaptive Personalized Quiz Creation System Is An Intelligent Learning Platform Designed To Enhance Individual Academic Performance Through Customized Assessments. The System Leverages Machine Learning And Natural Language Processing Techniques To Analyze Learner Profiles, Study Materials, And Performance History, Enabling The Automatic Generation Of Tailored Quizzes That Align With The User’s Knowledge Level And Learning Objectives. By Incorporating Adaptive Difficulty Adjustment, The System Gradually Modifies Question Complexity Based On Real-time Responses, Ensuring An Engaging And Effective Learning Experience. The Platform Supports Multiple Question Formats, Including Multiplechoice, Short Answers, And Conceptual Understanding Questions, Promoting Comprehensive Knowledge Evaluation. Additionally, PeerTutor AI Provides Instant Feedback And Performance Analytics, Allowing Learners To Identify Weak Areas And Track Their Progress. The System Aims To Promote Self-paced Learning, Improve Retention, And Provide A More Personalized Educational Approach Compared To Traditional Static Assessment Methods. This Project Demonstrates The Potential Of AI-driven Assessment Tools To Revolutionize Modern Education By Offering Scalable, Efficient, And Learner-centered Evaluation Solutions. Keywords:Adaptive Learning, Personalized Assessment, Quiz Generation, Machine Learning, Natural Language Processing (NLP), Difficulty Adjustment, Performance Analytics, Educational Technology, Intelligent Tutoring Systems, Learner Profiling. |
Published:11-12-2025 Issue:Vol. 25 No. 12 (2025) Page Nos:144-150 Section:Articles License:This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. How to CiteMs. Jahnavi Deepika Gubbala,Thandu Varshitha,Samala Bhavya Sri,Shanigarapu Haritha, PEERTUTOR AI: ADAPTIVE PERSONALIZED QUIZ CREATION SYSTEM , 2025, International Journal of Engineering Sciences and Advanced Technology, 25(12), Page 144-150, ISSN No: 2250-3676. |