AITEE —— 電氣工程自主導師

AITEE -- Agentic Tutor for Electrical Engineering

May 27, 2025

作者: Christopher Knievel, Alexander Bernhardt, Christian Bernhardt

cs.AI

摘要

智能導學系統與大型語言模型相結合，為滿足學生多樣化需求及促進自我效能學習提供了一種極具前景的方法。儘管大型語言模型具備良好的電氣工程基礎知識，但在處理電路相關的具體問題上仍顯不足。本文介紹了AITEE，一種基於代理的電氣工程導學系統，旨在伴隨學生整個學習過程，提供個性化支持，並推動自主學習。AITEE通過適應性的電路重建過程，支持手繪與數字電路，實現了與學生的自然互動。我們新穎的基於圖的相似度度量方法，通過檢索增強生成策略從講義材料中識別相關上下文，而並行的Spice模擬進一步提升了解決方法應用的準確性。該系統採用蘇格拉底式對話，通過引導性提問培養學習者自主性。實驗評估表明，AITEE在領域特定知識應用上顯著優於基準方法，即使是中等規模的LLM模型也展現了可接受的性能。我們的研究結果凸顯了代理型導師在為電氣工程教育提供可擴展、個性化且高效的學習環境方面的潛力。

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.