LLM-based intelligent system for quality control of student assignments

Abstract

This work explores the potential of large language models fo r automated quality control of student assignments through a human-in-the-loop approach. The proposed LLM-based intelligent system uses the generative abilities of language models to interact with experts and refine evaluation criteria dynamically. This adaptive process enhances grading accuracy, consistency, and interpretability, bringing automated assessment closer to human-level quality.