International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Preparing for academic assessments effectively remains a critical challenge for students and faculty alike, with many relying on traditional manual methods that lack personalization, scalability, and real-time feedback mechanisms. Current evaluation tools provide limited domain-specific guidance, lack semantic understanding of descriptive answers, and fail to capture the nuanced pedagogical criteria defined by frameworks like Bloom’s Taxonomy. The absence of adaptive, AI-driven assessment systems leaves students underprepared for complex, curriculum-aligned evaluations tailored to their specific subjects and cognitive levels.
To address these limitations, the EduRAG System integrates Generative AI, Retrieval-Augmented Generation (RAG), and Computer Vision to deliver personalized, interactive assessment at scale. The system leverages advanced NLP and transformer-based models to generate contextually relevant technical questions based on syllabus content and cognitive difficulty levels provided by users. Real-time Optical Character Recognition (OCR) captures handwritten student responses, while AI-powered evaluation mechanisms assess semantic accuracy and conceptual depth against industry-standard "Ground Truth" extracted from the curriculum. The application provides instant, detailed feedback including quantitative scoring, improvement suggestions, and performance analytics across multiple evaluation attempts. Through a user-centric web platform, faculty access role-specific question generation banks and students receive AI-generated recommendations for skill enhancement. By combining adaptive question generation with semantic answer analysis, the proposed solution significantly improves academic performance while democratizing access to high-quality pedagogical tools.

Keywords: Retrieval-Augmented Generation, Semantic Vectorization, Bloom’s Taxonomy, Automated Assessment.

Downloads: | DOI: 10.17148/IJARCCE.2026.151144