A Hybrid Automated Essay Scoring Framework Integrating Random Forest Regression and BERT-Based Semantic Analysis

EssayEval AI is a comprehensive automated essay scoring system that integrates machine learning and natural language processing to deliver immediate, detailed, and actionable feedback on student-written essays. The system combines a Random Forest Regressor trained on curated linguistic features with a BERT-based transformer model to produce holistic and component-level essay evaluations. Linguistic features including word count, unique word count, vocabulary richness ratio, and average word length capture writing quality dimensions related to lexical diversity and vocabulary sophistication. The BERT model contributes a semantic quality score derived from its pretrained language understanding. The composite score is computed as a weighted combination of these two components with weights of 0.6 and 0.4 respectively. A Streamlit-based interface presents grammar, vocabulary, and coherence sub-scores alongside tailored feedback within seconds of submission. The system is designed to democratize access to high-quality writing feedback in educational environments with limited instructor availability.