International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The increasing volume of written assessments in education has made manual essay evaluation time-consuming, subjective, and inconsistent. To address these challenges, this paper presents an Automated Essay Grading System that uses Natural Language Processing (NLP) and Machine Learning techniques to evaluate essays efficiently and fairly. The proposed system combines TF-IDF features and BERT-based embeddings to capture both statistical and contextual meaning of text, and employs a Ridge Regression model for accurate score prediction. In addition to grading, the system provides detailed feedback including grammar checking, readability analysis, sentiment evaluation, and bias detection, helping students improve their writing skills. A user-friendly web interface developed using Streamlit allows users to submit essays and view results instantly. The system also supports multilingual essay grading for regional languages such as English, Kannada, Telugu, and Tamil, making it more inclusive and accessible. Experimental results show that the system delivers consistent, scalable, and objective evaluation while significantly reducing grading effort. This work demonstrates the potential of AI-driven assessment tools in enhancing modern educational practices.

Keywords: Automated Essay Grading, Natural Language Processing, Machine Learning, BERT, TF-IDF, Ridge Regression, Multilingual Essay Evaluation, Educational Technology, AI-Based Assessment

Downloads: | DOI: 10.17148/IJARCCE.2026.15178