Multimodal AI Teacher: Integrating Edge Computing and Reasoning Models for Enhanced Student Error Analysis

Abstract

This paper extends our previously published work on the virtual AI teacher (VATE) system, presented at IAAI-25. VATE is designed to autonomously analyze and correct student errors in mathematical problem-solving using advanced large language models (LLMs). By incorporating student draft images as a primary input for reasoning, the system provides fine-grained error cause analysis and supports real-time, multi-round AI—student dialogues. In this extended version, we introduce a new snap-to-solve module for handling low-reasoning tasks using edge-deployed LLMs, enabling faster and partially offline interaction. We also include expanded benchmarking experiments, including human expert evaluations and ablation studies, to assess model performance and learning outcomes. Deployed on the Squirrel AI platform, VATE demonstrates high accuracy (78.3%) in error analysis and improves student learning efficiency, with strong user satisfaction. These results suggest that VATE is a scalable, cost-effective solution with the potential to transform educational practices.