AITEE -- Agentic Tutor for Electrical Engineering
Christopher Knievel, Alexander Bernhardt, Christian Bernhardt

TL;DR
AITEE is an agentic tutoring system for electrical engineering that combines retrieval-augmented generation, circuit simulation, and Socratic dialogue to provide personalized, scalable support for students, outperforming baseline methods.
Contribution
This paper introduces AITEE, a novel agent-based electrical engineering tutor that integrates graph-based retrieval, circuit simulation, and Socratic dialogue to enhance personalized learning.
Findings
AITEE significantly outperforms baseline approaches in domain knowledge application.
Medium-sized LLMs achieve acceptable performance with AITEE.
The system effectively supports both hand-drawn and digital circuits.
Abstract
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…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Innovative Teaching and Learning Methods · Text Readability and Simplification
