AITEE —— 电气工程智能辅导系统

AITEE -- Agentic Tutor for Electrical Engineering

May 27, 2025

作者: Christopher Knievel, Alexander Bernhardt, Christian Bernhardt

cs.AI

摘要

智能辅导系统与大型语言模型相结合，为满足学生多样化需求并促进自我效能学习提供了一种极具前景的途径。尽管大型语言模型具备电气工程基础知识的良好储备，但在处理关于电路的具体问题时仍显不足。本文介绍了AITEE，一个基于代理的电气工程辅导系统，旨在伴随学生学习全过程，提供个性化支持，并推动自主学习。AITEE通过适配的电路重建过程，支持手绘与数字电路，实现了与学生的自然交互。我们新颖的基于图的相似度度量方法，通过检索增强生成策略从讲义材料中识别相关上下文，而并行的Spice仿真则进一步提升了解决方案方法的应用准确性。系统采用苏格拉底式对话，通过引导性问题培养学习者自主性。实验评估表明，AITEE在领域特定知识应用上显著优于基线方法，即使是中等规模的大型语言模型也展现出可接受的性能。我们的成果凸显了代理型辅导系统在电气工程教育中提供可扩展、个性化且高效学习环境的巨大潜力。

English

Intelligent tutoring systems combined with large language models offer a promising approach to address students' diverse needs and promote self-efficacious learning. While large language models possess good foundational knowledge of electrical engineering basics, they remain insufficiently capable of addressing specific questions about electrical circuits. In this paper, we present AITEE, an agent-based tutoring system for electrical engineering designed to accompany students throughout their learning process, offer individualized support, and promote self-directed learning. AITEE supports both hand-drawn and digital circuits through an adapted circuit reconstruction process, enabling natural interaction with students. Our novel graph-based similarity measure identifies relevant context from lecture materials through a retrieval augmented generation approach, while parallel Spice simulation further enhances accuracy in applying solution methodologies. The system implements a Socratic dialogue to foster learner autonomy through guided questioning. Experimental evaluations demonstrate that AITEE significantly outperforms baseline approaches in domain-specific knowledge application, with even medium-sized LLM models showing acceptable performance. Our results highlight the potential of agentic tutors to deliver scalable, personalized, and effective learning environments for electrical engineering education.