# On the effectiveness of LLMs for automatic grading of open-ended questions in Spanish

arXiv: https://arxiv.org/abs/2503.18072 · HTML: https://mlsanity.com/paper/2503.18072/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Simulating Classroom Education with LLM-Empowered Agents](https://mlsanity.com/paper/2406.19226/) (2024)
- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes](https://mlsanity.com/paper/2412.19260/) (2024)
- [Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation](https://mlsanity.com/paper/2404.15845/) (2024)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [Towards LLM-based Autograding for Short Textual Answers](https://mlsanity.com/paper/2309.11508/) (2023)
- [Grade Like a Human: Rethinking Automated Assessment with Large Language Models](https://mlsanity.com/paper/2405.19694/) (2024)
- [A LLM-Powered Automatic Grading Framework with Human-Level Guidelines Optimization](https://mlsanity.com/paper/2410.02165/) (2024)

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

- [A Human-in-the-Loop Framework for AI-Assisted Scoring in Large-Scale Writing Assessment](https://mlsanity.com/paper/2609.05143/) (2026)

## Abstract (arXiv)

Grading is a time-consuming and laborious task that educators must face. It is an important task since it provides feedback signals to learners, and it has been demonstrated that timely feedback improves the learning process. In recent years, the irruption of LLMs has shed light on the effectiveness of automatic grading. In this paper, we explore the performance of different LLMs and prompting techniques in automatically grading short-text answers to open-ended questions. Unlike most of the literature, our study focuses on a use case where the questions, answers, and prompts are all in Spanish. Experimental results comparing automatic scores to those of human-expert evaluators show good outcomes in terms of accuracy, precision and consistency for advanced LLMs, both open and proprietary. Results are notably sensitive to prompt styles, suggesting biases toward certain words or content in the prompt. However, the best combinations of models and prompt strategies, consistently surpasses an accuracy of 95% in a three-level grading task, which even rises up to more than 98% when the it is simplified to a binary right or wrong rating problem, which demonstrates the potential that LLMs have to implement this type of automation in education applications.