International Scientific Journal of Engineering and Management

An International Scholarly || Multidisciplinary || Open Access || Indexing in all major Database & Metadata
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ISSN: 2583-6129

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An AI-Driven Automated Answer Script Assessment and Grading System using Natural Language Processing, OCR, and Machine Learning Techniques for Smart Educational Evaluation

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Published 10 May 2026
Updated 10 May 2026

An AI-Driven Automated Answer Script Assessment and Grading System using Natural Language Processing, OCR, and Machine Learning Techniques for Smart Educational Evaluation

 

 

 

K Pranathi, Karuna K Naik, Kaveri, R Poojitha

*1Department of Artificial Intelligence & Machine Learning, Ballari Institute of Technology & Management, Ballari, Karnataka, India

*2Department of Artificial Intelligence & Machine Learning, Ballari Institute of Technology & Management, Ballari, Karnataka, India

*3Department of Artificial Intelligence & Machine Learning, Ballari Institute of Technology & Management, Ballari, Karnataka, India

*4Department of Artificial Intelligence & Machine Learning, Ballari Institute of Technology & Management, Ballari, Karnataka, India

Dr. Mallikarjuna A(Professor)

Department of Artificial Intelligence & Machine Learning,

Ballari Institute of Technology & Management, Ballari, Karnataka, India

 

 

 

ABSTRACT:The rapid growth of digital education has fueled demand for automated evaluation systems that deliver fast, accurate, and fair student assessments, overcoming the limitations of manual grading like time consumption, inconsistency, and lack of personalized feedback. This project introduces AAVESUMETE, an Automated Answer Script and Grading System powered by Natural Language Processing (NLP) models such as sentence embeddings and cosine similarity to evaluate responses based on keyword relevance, grammar, structure, and context, assigning scores, grades, and constructive feedback. Supporting three key roles—Teachers (for secure login, uploading question papers, model answers, and student submissions while configuring quizzes), Students (for profile management, result access, and feedback review to enhance learning), and Heads of Department (HODs, for monitoring activities, class performance tracking, and subject-wise analytics)—the system promotes transparency, scalability, and efficiency through real-time dashboards and multi-role access, transforming traditional exams into a data-driven, student-centric process.

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