Agentic AI for Automated Essay Scoring

This research project aims to develop an AI-powered essay grading system that is both cost-effective and highly accurate. The project will explore how a dual-agent AI system, one that optimizes the cost and another that performs prompt refinement, can improve automated essay scoring at scale. By using advanced techniques such as Retrieval-Augmented Generation (RAG) and error-based prompt refinement, the system will ensure more precise and consistent grading. The partner organization, a leading educational service provider, will benefit from operational improvements, faster feedback for students, and improved accuracy in assessments. Additionally, the system’s open-source, on-premise design ensures student data security. Overall, this project will not only enhance the partner organization’s competitiveness but also strengthen its reputation as a leader in AI-driven education. By improving grading efficiency and educational outcomes, the project will contribute to the broader goal of making AI a valuable tool in modern education.

Faculty Supervisor:

Mucahit Cevik

Student:

Partner:

Blees AI

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Toronto Metropolitan University

Program: