# Automated Assignment Grading with Large Language Models: Insights From a Bioinformatics Course

arXiv: https://arxiv.org/abs/2501.14499 · HTML: https://mlsanity.com/paper/2501.14499/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [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)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023)
- [HelpSteer2-Preference: Complementing Ratings with Preferences](https://mlsanity.com/paper/2410.01257/) (2024)
- [A Comprehensive Evaluation of Quantization Strategies for Large Language Models](https://mlsanity.com/paper/2402.16775/) (2024)
- [Evaluating the Effectiveness of LLMs in Introductory Computer Science Education: A Semester-Long Field Study](https://mlsanity.com/paper/2404.13414/) (2024)
- [Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course](https://mlsanity.com/paper/2407.05216/) (2024)
- [Using Large Language Models for Automated Grading of Student Writing about Science](https://mlsanity.com/paper/2412.18719/) (2024)
- [Evaluating Students' Open-ended Written Responses with LLMs: Using the RAG Framework for GPT-3.5, GPT-4, Claude-3, and Mistral-Large](https://mlsanity.com/paper/2405.05444/) (2024)
- [Investigating Automatic Scoring and Feedback using Large Language Models](https://mlsanity.com/paper/2405.00602/) (2024)
- [Contextualizing Problems to Student Interests at Scale in Intelligent Tutoring System Using Large Language Models](https://mlsanity.com/paper/2306.00190/) (2023)

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

- [PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback](https://mlsanity.com/paper/2606.20287/) (2026)
- [Implicit Grading Bias in Large Language Models: How Writing Style Affects Automated Assessment Across Math, Programming, and Essay Tasks](https://mlsanity.com/paper/2603.18765/) (2026)
- [LLM-as-a-Grader: Practical Insights from Large Language Model for Short-Answer and Report Evaluation](https://mlsanity.com/paper/2511.10819/) (2025)

## Abstract (arXiv)

Providing students with individualized feedback through assignments is a cornerstone of education that supports their learning and development. Studies have shown that timely, high-quality feedback plays a critical role in improving learning outcomes. However, providing personalized feedback on a large scale in classes with large numbers of students is often impractical due to the significant time and effort required. Recent advances in natural language processing and large language models (LLMs) offer a promising solution by enabling the efficient delivery of personalized feedback. These technologies can reduce the workload of course staff while improving student satisfaction and learning outcomes. Their successful implementation, however, requires thorough evaluation and validation in real classrooms. We present the results of a practical evaluation of LLM-based graders for written assignments in the 2024/25 iteration of the Introduction to Bioinformatics course at the University of Ljubljana. Over the course of the semester, more than 100 students answered 36 text-based questions, most of which were automatically graded using LLMs. In a blind study, students received feedback from both LLMs and human teaching assistants without knowing the source, and later rated the quality of the feedback. We conducted a systematic evaluation of six commercial and open-source LLMs and compared their grading performance with human teaching assistants. Our results show that with well-designed prompts, LLMs can achieve grading accuracy and feedback quality comparable to human graders. Our results also suggest that open-source LLMs perform as well as commercial LLMs, allowing schools to implement their own grading systems while maintaining privacy.