Project Description:

The AI-Based Essay Scoring System is an intelligent application that automatically evaluates and scores essays written by students or users based on multiple criteria such as grammar, coherence, relevance, vocabulary, and structure. It uses Natural Language Processing (NLP) and Machine Learning (ML) models to mimic human evaluators in assessing essays fairly and consistently.

The system provides instant feedback, detailed scoring breakdowns, and recommendations for improvement, helping students and educators save time and improve writing skills.

Core Objectives:

• Automatically evaluate essays and generate scores across multiple parameters.

• Provide detailed feedback on grammar, structure, and content relevance.

• Enable essay submission, tracking, and progress over time.

Key Features:

 1. Essay Submission Portal

• Students can:

  • Submit essays through a rich text editor.

  • Select essay type (narrative, persuasive, analytical, etc.)

• Teachers can:

  • View all submissions.

  • Compare AI scores with human scores (optional).

 2. AI-Powered Essay Evaluation

• Evaluates essays based on:

  • Grammar & spelling

  • Sentence structure & coherence

  • Vocabulary richness

  • Relevance to topic

  • Overall writing quality

• Uses pre-trained NLP models (like BERT, RoBERTa, or GPT-based models).

 3. Automated Scoring System

• Assigns score (e.g., out of 10 or 100) with breakdown:

  • Language Mechanics: 20%

  • Structure & Organization: 20%

  • Content Relevance: 30%

  • Vocabulary Usage: 15%

  • Creativity & Originality: 15%

 4. Feedback Generator

• Highlights weak areas (e.g., grammar mistakes, poor transitions)

• Suggests:

  • Better vocabulary

  • Improved sentence structures

  • More relevant content based on prompt

 5. User Dashboard

• Students: View all submitted essays, scores, feedback, and progress over time

• Teachers: View class performance analytics, flag AI misjudgments

6. Plagiarism Checker (Optional)

• Detects and flags copied content from online sources.

Tech Stack:

Frontend:

• HTML, CSS, Bootstrap – UI design

• JavaScript – interactivity and validation

Backend:

• PHP / Node.js / Java – handles requests, logic, user roles

• Integration with AI scoring engine (Python via API)

AI/ML/NLP Engine:

• Python (Flask or FastAPI)

• Libraries:

  • spaCy / NLTK / Transformers (HuggingFace) for text processing

  • scikit-learn / TensorFlow / PyTorch for model training/inference

• Trained on open essay datasets like ASAP-AES

Database:

• MySQL / PostgreSQL – stores essays, scores, users