Abstract :

The Increasing Volume Of Academic And Examination Papers Has Highlighted The Need For Efficient, Consistent, And Objective Evaluation Methods. Traditional Manual Grading Is Time-consuming, Prone To Human Bias, And Often Inconsistent. This Research Proposes An AI-Based Assessment Framework For Automated Paper Evaluation That Leverages Advanced Natural Language Processing (NLP) And Machine Learning (ML) Techniques To Evaluate Written Responses Accurately And Efficiently. The Framework Extracts Textual, Semantic, And Structural Features From Student Answers, Including Grammar, Coherence, Relevance, And Concept Coverage. These Features Are Processed Using A Trained AI Model To Assign Scores And Provide Constructive Feedback Automatically. Experimental Results Demonstrate That The Proposed System Achieves High Correlation With Expert Human Grading, Ensuring Reliability, Fairness, And Scalability. This Framework Offers A Practical Solution For Academic Institutions And Online Learning Platforms, Significantly Reducing Grading Time While Maintaining Evaluation Quality. Keywords: Automated Evaluation, AI-Based Assessment, Natural Language Processing, Machine Learning, Paper Grading, Semantic Analysis, Educational Technology, Automated Feedback.

Published:

18-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

299-305

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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