Automated Essay Scoring System (AESS)
Overview:
This project tackles the challenges of traditional essay grading by creating an AI-powered system that provides efficient, scalable, and objective feedback to students.
Tech Stack:
- Frontend: Next.js, React
- Backend: FastAPI (Python)
- Natural Language Processing (NLP): spaCy, Language Tools
- Large Language Model (LLM): Gemini
- Database: MongoDB
- Additional Services: Firebase
Project Description:
- Students securely login using social accounts (e.g., Google).
- Essays can be uploaded (PDF, DOCX, TXT) or directly typed.
- The system analyzes essays using NLP for grammar, spelling, and readability.
- A Retrieval-Augmented Generation (RAG) model retrieves relevant rubrics (currently hardcoded).
- A Large Language Model (LLM) scores the essay based on rubrics and generates personalized feedback.
- Students receive feedback reports with overall scores, rubric-specific breakdowns, suggestions for improvement, and readability analysis.
Project Objectives & Scope:
- Automate essay grading to save teacher time and effort.
- Provide objective feedback based on pre-defined rubrics.
- Reduce subjectivity and bias in grading.
- Offer students personalized suggestions for improvement.
- Enhance the efficiency and effectiveness of essay evaluation.
Technical Challenges:
- Ensuring accurate and unbiased scoring through NLP and LLM.
- Developing a robust RAG model for dynamic rubric selection (future work).
- Integrating the system with existing learning platforms (future work).
What Was Improved:
- Essay grading efficiency and scalability.
- Objectivity and consistency of feedback.
- Student understanding of strengths and weaknesses in writing.
- Identification and correction of grammatical errors.
How It Works:
- Student logs in securely using Google account.
- Essay is uploaded or directly typed.
- System analyzes essay using NLP and LLM.
- RAG model retrieves relevant rubrics (currently hardcoded).
- LLM scores the essay based on rubrics and generates feedback.
- Student receives a comprehensive feedback report.
Customer Satisfaction (Future Work):
- Collect student feedback on the clarity, helpfulness, and accuracy of the feedback reports.
- Analyze feedback to identify areas for improvement.
Screenshots
Login Page
Essay Upload Page
Feedback Report Page
Metrics Page
Suggestions Page
Feedback Page
Essay Statistics Page
