An Automated Educational Assessment System for Personalized Learning Using an AI-Driven Feedback and Micro-Quiz Generation

Abstract

: Traditional educational assessment systems often prioritize grading over learning, falling short in automating the evaluation of complex coding and subjective responses. This introduces inconsistencies and slows the feedback cycle. This paper introduces InsightEval, an AI-powered system designed to transform static assessments into dynamic tools for continuous learning. It automates the evaluation of multiple-choice, coding, and subjective questions by integrating technologies like Judge0 for code execution and Large Language Models for natural language grading. The system’s core innovation is a 'Feedback -to-Improvement Loop,' which analyzes incorrect answers using NLP to identify conceptual gaps and then generates personalized micro-quizzes for targeted reinforcement. It also visualizes topic interconnections through Concept Linkage Maps, helping learners and teachers track conceptual mastery. InsightEval delivers an interactive, feedback-driven experience that promotes deeper understanding and continuous academic growth.