# CodEv: An Automated Grading Framework Leveraging Large Language Models for Consistent and Constructive Feedback

arXiv: https://arxiv.org/abs/2501.10421 · HTML: https://mlsanity.com/paper/2501.10421/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://mlsanity.com/paper/2305.02301/) (2023)
- [GPTScore: Evaluate as You Desire](https://mlsanity.com/paper/2302.04166/) (2023)
- [More Agents Is All You Need](https://mlsanity.com/paper/2402.05120/) (2024)
- [Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference](https://mlsanity.com/paper/2403.20306/) (2024)
- [Automated Grading and Feedback Tools for Programming Education: A Systematic Review](https://mlsanity.com/paper/2306.11722/) (2023)

## Built on by (most cited first, 3 of 3)

- [Rubric Is All You Need: Enhancing LLM-based Code Evaluation With Question-Specific Rubrics](https://mlsanity.com/paper/2503.23989/) (2025)
- [Autograder+: A Multi-Faceted AI Framework for Rich Pedagogical Feedback in Programming Education](https://mlsanity.com/paper/2510.26402/) (2025)
- [Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach](https://mlsanity.com/paper/2607.02432/) (2026)

## Abstract (arXiv)

Grading programming assignments is crucial for guiding students to improve their programming skills and coding styles. This study presents an automated grading framework, CodEv, which leverages Large Language Models (LLMs) to provide consistent and constructive feedback. We incorporate Chain of Thought (CoT) prompting techniques to enhance the reasoning capabilities of LLMs and ensure that the grading is aligned with human evaluation. Our framework also integrates LLM ensembles to improve the accuracy and consistency of scores, along with agreement tests to deliver reliable feedback and code review comments. The results demonstrate that the framework can yield grading results comparable to human evaluators, by using smaller LLMs. Evaluation and consistency tests of the LLMs further validate our approach, confirming the reliability of the generated scores and feedback.