Paper Key : IRJ************371
Author: Mrudula S. Yeotkar,Saloni P. Ghodki,Samiksha G. Kalaskar,Samruddhi R. Bodkhe,Vaishali B. Bambode
Date Published: 06 Apr 2025
Abstract
Artificial Intelligence (AI) and machine learning are transforming education by making learning more personalized. This research presents an AI-driven adaptive learning system that improves traditional multiple-choice question (MCQ)-based assessments. The system analyzes studentsresponses by looking at accuracy, speed, and consistency to provide real-time feedback and adjust question difficulty accordingly. This ensures that students receive questions suited to their learning pace, keeping them engaged and improving their understanding. The system also tracks student progress over time, identifying areas where they struggle and adapting learning materials to help them improve. By using data-driven insights, it selects questions that challenge students at the right level without overwhelming them. Additionally, predictive analytics help educators understand students future performance and make better teaching decisions. This approach offers a scalable and intelligent solution to enhance student learning by delivering personalized content and feedback. AI-driven adaptive learning can bridge gaps in traditional education, making learning more interactive, efficient, and tailored to individual needs.