Monroe Grading Agent: AI-Powered Automated Assignment Grading in Higher Education

Automated grading has emerged as a valuable approach to address the heavy workload and consistency challenges of manual grading in higher education. Instructors often spend countless hours evaluating student assignments, which can delay feedback and introduce subjective inconsistencies. Prior research highlights the efficiency and objectivity of AI-assisted grading, noting its potential to reduce grading inconsistencies by up to 44% (Yigit et al., 2024) and deliver faster, more scalable feedback (Wang et al., 2022). This paper introduces the Monroe Grading Agent (MGA) – an AI-powered desktop application developed to automatically grade student semester assignments by comparing submissions against the assignment instructions. MGA utilizes TF-IDF vectorization, cosine similarity, and a novel cube-root scaling method to provide fairer, partial-credit-based grading. Evaluated using realistic Monroe University assignment data, MGA demonstrates significant improvements in grading efficiency, scoring consistency, and feedback richness. A before-and-after analysis shows that MGA can drastically reduce grading time while maintaining scores comparable to human evaluators. The system also generates structured feedback aligned with assignment criteria. The paper features GUI screenshots, contextualizes MGA within the broader automated grading literature, and explores implications for teaching faculty. It concludes by outlining future enhancements including LMS integration, rubric-based scoring, and support for programming assignments, positioning MGA as a practical and scalable solution for modern academic environments.