# AITEE -- Agentic Tutor for Electrical Engineering

arXiv: https://arxiv.org/abs/2505.21582 · HTML: https://mlsanity.com/paper/2505.21582/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 27 May 2025.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 20 of 20)

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- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
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- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
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- [RAG-Fusion: a New Take on Retrieval-Augmented Generation](https://mlsanity.com/paper/2402.03367/) (2024)
- [Enhancing Critical Thinking in Education by means of a Socratic Chatbot](https://mlsanity.com/paper/2409.05511/) (2024)
- [SPL: A Socratic Playground for Learning Powered by Large Language Model](https://mlsanity.com/paper/2406.13919/) (2024)
- [How to Build an Adaptive AI Tutor for Any Course Using Knowledge Graph-Enhanced Retrieval-Augmented Generation (KG-RAG)](https://mlsanity.com/paper/2311.17696/) (2023)
- [Hand-Drawn Electrical Circuit Recognition using Object Detection and Node Recognition](https://mlsanity.com/paper/2106.11559/) (2021)
- [Deep Metric Learning for Computer Vision: A Brief Overview](https://mlsanity.com/paper/2312.10046/) (2023)
- [ElectroVizQA: How well do Multi-modal LLMs perform in Electronics Visual Question Answering?](https://mlsanity.com/paper/2412.00102/) (2024)
- [Enhancing classroom teaching with LLMs and RAG](https://mlsanity.com/paper/2411.04341/) (2024)

## Abstract (arXiv)

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.