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AI-Powered Automated Grading System for Scalable Student Assessment

gogoapps.io

Education

Information technology

Challenges in Scaling Manual Grading Processes

Manual grading processes at scale lead to excessive time consumption, high operational costs, delayed student feedback, resource constraints, and risks to student privacy. Existing solutions lack customization, scalability, and cost-efficiency.

About the Client

Innovative university focused on education technology advancements

Objectives for AI Grading System Development

Create a proof-of-concept AI grading system to automate feedback generation
Reduce student feedback delivery time from days to hours
Augment human graders' efficiency without replacing them
Ensure cost-effective and maintainable solution architecture
Implement robust data privacy and security measures

Core System Functionalities

API integration with Learning Management Systems (LMS)
LLM-based automated feedback generation using Tree of Thought processing
Secure JWT token authentication and data encryption
Dockerized microservices for cloud-agnostic deployment
User interface for graders to review and finalize AI-generated feedback

Technology Stack Requirements

Python

JWT

Docker

Langchain

ChatGPT API

AWS

System Integration Needs

Canvas
Moodle
Blackboard LMS platforms

Critical Non-Functional Requirements

Horizontal scalability for 10x user growth
End-to-end encryption for student data
HIPAA/GDPR compliance
99.9% system availability SLA
Seamless key rotation for security management

Expected Business Impact

Enables 80% faster feedback delivery, 60% reduction in grading costs, and 95% improvement in feedback consistency while maintaining strict privacy compliance. Scalable architecture supports 100,000+ concurrent users with minimal infrastructure overhead.