Manually evaluating essays is time-consuming, subjective, and labor-intensive for educators. Different raters may score the same essay differently due to personal biases. An automatic essay scoring (AES) system provides consistent, fast, and objective evaluation of writing skills. By analyzing grammar, vocabulary, coherence, structure, and content, machine learning models can score essays accurately and provide actionable feedback to learners.
Using natural language processing (NLP) pipelines, essays can be tokenized, analyzed for linguistic features, and evaluated using regression models. Features like grammar errors, sentence complexity, cohesion, vocabulary richness, and topic relevance are extracted. ML models predict essay scores by learning from human-graded samples, ensuring reliable, explainable, and scalable grading systems for educational platforms and institutions.
Save time and ensure fairness by providing instant, objective, and bias-free essay grading across large numbers of students.
Work with advanced natural language processing techniques, feature engineering, and predictive modeling for real-world education technology applications.
Automatic essay grading supports personalized learning by offering instant feedback, helping students identify areas of improvement faster.
Showcase your ability to build intelligent learning systems by combining education domain knowledge with cutting-edge AI techniques.
Essays are input as text, and NLP pipelines preprocess them by tokenizing, removing stopwords, and extracting linguistic features. Models analyze writing quality based on coherence, grammar, sentence variation, lexical diversity, and semantic relevance. Regression or ranking models trained on human-graded datasets predict essay scores. Feedback modules can suggest writing improvements based on grammar errors, vocabulary gaps, or coherence issues detected during analysis.
spaCy, NLTK, Hugging Face Transformers, scikit-learn, XGBoost for feature extraction and modeling
Python (pandas, NumPy, TextBlob, LanguageTool for grammar checks)
Streamlit, Flask, or React.js frontend integrated with backend scoring APIs
ASAP Automated Student Assessment Prize Dataset, TOEFL11 Dataset for essay scoring
Collect human-graded essay datasets, clean text, perform standard NLP preprocessing, and prepare feature sets for modeling.
Extract structural, lexical, syntactic, and semantic features from essays for training predictive models.
Train regression or ranking models to predict essay scores, ensuring model generalization across different essay prompts.
Evaluate using metrics like RMSE (Root Mean Squared Error), Quadratic Weighted Kappa (QWK) to compare predictions with human scores.
Deploy a web platform where students/teachers can upload essays and instantly receive AI-driven scoring and detailed writing feedback.
Revolutionize education by building an AI system that fairly, instantly, and intelligently scores essays — let’s get started today!
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