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

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.