AITEE -- Agentic Tutor for Electrical Engineering
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.
68380c860fb1ddbe91ba0bf9
Christopher Knievel
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2025-05-29T07:41:12.986Z
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Christopher Knievel
user
CKnievel
68380c860fb1ddbe91ba0bfa
Alexander Bernhardt
68380c860fb1ddbe91ba0bfb
Christian Bernhardt
AITEE -- Agentic Tutor for Electrical Engineering
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.
An agent-based tutoring system for electrical engineering enhances learning through natural circuit interaction, context-retrieving generation, and guided questioning, demonstrating superior performance compared to baseline methods.
agent-based tutoring system large language models electrical circuits adapted circuit reconstruction graph-based similarity measure retrieval augmented generation parallel Spice simulation Socratic dialogue