Research

EduMark AI: AI-Driven Grading and Personalised Student Feedback to Save Educator Time

Principal investigator: Deepshikha DEEPSHIKHA
Co-investigator(s): Li WANG, Xinru DENG, Giuseppe VIOLA and Mouna CHETEHOUNA
Funding source(s): QMUL President and Principal's Fund for Educational Excellence
 Start: 01-08-2024  /  End: 31-08-2025
This project addresses the pressing need for efficiency and consistency in grading and feedback processes at Queen Mary University of London (QMUL), aligning with QMUL's Strategy 2030 for digital transformation and educational excellence. EduMark AI seeks to integrate artificial intelligence to streamline grading tasks and enhance the quality of feedback for students, aiming to reduce assessment time by 50%. Through collaboration with key QMUL stakeholders, the project will evaluate three AI systems (e.g., ChatGPT, Google AI Gemini, and Graide) on their effectiveness in grading and feedback quality. The project will assess AI's impact on educator workload and student satisfaction by conducting a controlled comparison with traditional grading methods. This initiative contributes to the broader QMUL goal of adaptive, personalised education. It aims to establish best practices for scalable AI integration across disciplines, benefiting faculty, students, and QMUL's academic community.