Authors : Naman Agnihotri; Harshvardhan Grandhi; Dhanashri Patil; Sanika Kharade

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/2fjc2zn5

Scribd : https://tinyurl.com/495zter8

DOI : https://doi.org/10.38124/ijisrt/25mar1924

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Abstract : This paper introduces an AI-powered exam assessment system designed to automate the evaluation of handwritten answer sheets, encompassing both textual answers and diagrams. The system addresses the inherent limitations of traditional manual grading methods, such as their labor-intensive nature, susceptibility to human error, and time consumption. In contrast to conventional Optical Character Recognition (OCR) solutions that struggle with handwriting diversity and visual content, the proposed system directly interprets both text and visual data, enabling accurate and efficient grading of diverse student responses. By leveraging AI models with multimodal capabilities, the system effectively compares student answers with predefined question papers and answer keys to ensure objective and consistent grading. This innovative approach offers a scalable and cost-effective solution for educational institutions, significantly reducing the time and resources required for manual evaluations while enhancing the accuracy and fairness of the assessment process.

Keywords : Large Language Models (LLMs), Vision Language Models (VLMs), Handwritten Answer Assessment, Automated Grading, AI in Education, Multimodal Assessment, Diagram Evaluation, Scalable Assessment System.

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