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 the Squirrel AI platform that autonomously analyzes student mathematical errors from their work drafts, provides targeted error explanations, and engages in multi-round Socratic dialogue to guide correction. The system achieves 78.3% accuracy in error analysis for elementary mathematics and demonstrates improved student learning efficiency in real classroom deployment.

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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