An Intelligent System for Automated Answer Sheet Evaluation Using Artificial Intelligence | IJCT Volume 13 – Issue 3 | IJCT-V13I3P28
International Journal of Computer Techniques
ISSN 2394-2231
Author
Shruti Mishra, Ram Kumar
Abstract
The education sector is rapidly evolving with the integration of Artificial Intelligence (AI) and Machine Learning technologies. Despite advancements in digital learning, the evaluation of descriptive answer sheets remains largely manual, time-consuming, and prone to human bias. This research paper proposes an AI-Based Answer Sheet Evaluation System in which teachers upload question papers, predefined answer keys, and scanned student answer sheets. The system utilizes Optical Character Recognition (OCR) to extract text from scanned documents and Natural Language Processing (NLP) techniques to evaluate student answers based on semantic similarity with the answer key. The proposed approach ensures unbiased evaluation, faster result generation, and scalability for large academic institutions.
Keywords
Artificial Intelligence, Answer Sheet Evaluation, OCR, NLP, Automated Assessment, Machine Learning
Conclusion
The AI-based Answer Sheet Evaluation System provides an efficient and reliable solution to automate answer evaluation. By leveraging AI, NLP, and OCR, the system significantly reduces manual effort and improves accuracy. This project demonstrates the potential of artificial intelligence in transforming the education sector. The “Digital Handwritten Answer Sheet Evaluation System” project represents an important advance in the field of education and can completely change the way that students are evaluated. This approach reduces the burden on educators and promotes better student learning experiences by addressing long-standing problems including time- consuming manual grading, inconsistent assessment standards, subjective evaluation, delayed feedback, and scalability concerns. It provides advantages including enhanced efficiency, objectivity, and quick feedback provided through automation
References
1.Jurafsky, D. & Martin, J. H., Speech and Language Processing
2.Manning, C. D., Introduction to Information Retrieval
3.Research papers on NLP-based assessment systems
4.Smith, J., Johnson, A., & Williams,
B. Challenges in Manual Grading of Handwritten Answer Sheets: A
Review. “Journal of Educational Assessment”, 12(3), 145-162,(2017).
Kim, S., & Lee, J. Automated Grading of Handwritten Answer Sheets Using OCR and NLP. “Journal of Educational Technology, 28(1), 78-92, (2022).
5.Li,M., etal. Integrating OCR and NLP for Automated Assessment of Handwritten Responses. “International Conference on
Educational Technology”, 132-145,
(2021).
6.Liu, Y., et al. Deep Learning Approaches for Automated Grading of Mathematical Expressions. “IEEE Transactions on Learning Technologies”, 13(5), 1138-1152,
(2020).
How to Cite This Paper
Shruti Mishra, Ram Kumar (2026). An Intelligent System for Automated Answer Sheet Evaluation Using Artificial Intelligence. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
