# Enhancing Large Language Models for Automated Homework Assessment in Undergraduate Circuit Analysis

arXiv: https://arxiv.org/abs/2511.18221 · HTML: https://mlsanity.com/paper/2511.18221/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Circuits and mechanistic analysis, one of 13 directions in Mechanistic interpretability.
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Placed in

- Mechanistic interpretability / [Circuits and mechanistic analysis](https://mlsanity.com/mechinterp/circuits-and-mechanistic-analysis/)

## Builds on (most cited first, 15 of 15)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [Eureka: Human-Level Reward Design via Coding Large Language Models](https://mlsanity.com/paper/2310.12931/) (2023)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection](https://mlsanity.com/paper/2503.18132/) (2025)
- [RLingua: Improving Reinforcement Learning Sample Efficiency in Robotic Manipulations With Large Language Models](https://mlsanity.com/paper/2403.06420/) (2024)
- [Benchmarking Large Language Models for Math Reasoning Tasks](https://mlsanity.com/paper/2408.10839/) (2024)
- [CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs](https://mlsanity.com/paper/2502.07980/) (2025)
- [Benchmarking Large Language Models on Homework Assessment in Circuit Analysis](https://mlsanity.com/paper/2506.06390/) (2025)
- [WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis](https://mlsanity.com/paper/2506.08962/) (2025)

## Built on by (most cited first, 1 of 1)

- [EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions](https://mlsanity.com/paper/2602.00095/) (2026)

## Abstract (arXiv)

This research full paper presents an enhancement pipeline for large language models (LLMs) in assessing homework for an undergraduate circuit analysis course, aiming to improve LLMs' capacity to provide personalized support to electrical engineering students. Existing evaluations have demonstrated that GPT-4o possesses promising capabilities in assessing student homework in this domain. Building on these findings, we enhance GPT-4o's performance through multi-step prompting, contextual data augmentation, and the incorporation of targeted hints. These strategies effectively address common errors observed in GPT-4o's responses when using simple prompts, leading to a substantial improvement in assessment accuracy. Specifically, the correct response rate for GPT-4o increases from 74.71% to 97.70% after applying the enhanced prompting and augmented data on entry-level circuit analysis topics. This work lays a foundation for the effective integration of LLMs into circuit analysis instruction and, more broadly, into engineering education.