GradeZen: automated grading ecosystem using deep learning for educational assessments - Elangovan, Murugavalli; Kaleeswaran, Rajeswari; Mohankumar, Kathirvel; Rangachari, Shreya; Manivannan, Ubasini; Pandidurai, Rishapa; Bellarmin Joel, Maria; Murugesan, Preethi Meenatchi (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
This study introduces a groundbreaking software solution poised to revolutionize grading procedures in higher education through advanced artificial intelligence and machine learning techniques. Leveraging cutting-edge technologies such as YOLOv8 for real-time object detection, transformer-based optical character recognition (TrOCR), and Mixtral 8x7B transformer models for complex data analysis, the software automates the grading process. By significantly reducing the time and effort required for manual grading, it aims to streamline educational practices while ensuring consistency and scalability. The study provides a comprehensive analysis of use cases, identifies key issues in current grading methods, and elucidates the rationale driving its development. This innovative approach holds immense promise for transforming educational practices, fostering student success through efficient and artificial intelligence assisted automated assessment methodologies.