The proposed system provides an AI-based approach to the automated grading of descriptive examination answers using Natural Language Processing (NLP) and Machine Learning (ML) techniques. As student populations and digital learning environments grow, traditional manual evaluation methods become time consuming, subjective, and prone to inconsistencies. To overcome these problems, this system allows for the automatic evaluation of student responses based on keyword matching, semantic similarity and trained machine learning models. It allows real-time evaluation, reduces human bias and ensures scalability across multiple subjects and question types. The system is implemented as a web-based application with separate modules for students and administrators for answer submission, evaluation and performance monitoring. The experimental results show that the system has the accuracy of up to 88% which is in close proximity with human grading and saves a lot of time in evaluation. The system makes the examination process more efficient, consistent and transpare
KRUSHNA et al. (Fri,) studied this question.