AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction

This paper presents VATE (Virtual AI Teacher for Error Analysis), an LLM-based system deployed on Squirrel AI's platform that automatically analyzes student mathematical errors from their draft work, provides targeted feedback through multi-round dialogue, and guides students to correct mistakes. The system achieves 78.3% accuracy in error analysis for elementary mathematics and demonstrates improved learning efficiency compared to traditional methods.

Students frequently make mistakes while solving mathematical problems, and traditional error correction methods are both time-consuming and labor-intensive. This paper introduces an innovative Virtual AI Teacher system designed to autonomously analyze and correct student Errors (VATE). Leveraging advanced large language models (LLMs) like GPT-4, the system uses student drafts as a primary source for error analysis, which enhances understanding of the student's learning process. It incorporates s

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