Automatic Essay Scoring and Feedback Generation in Basque Language Learning

February 09, 2026

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📝 Original Info

Title: Automatic Essay Scoring and Feedback Generation in Basque Language Learning
ArXiv ID: 2512.08713
Date: 2025-12-09
Authors: Ekhi Azurmendi, Xabier Arregi, Oier Lopez de Lacalle

📝 Abstract

This paper introduces the first publicly available dataset for Automatic Essay Scoring (AES) and feedback generation in Basque, targeting the CEFR C1 proficiency level. The dataset comprises 3,200 essays from HABE, each annotated by expert evaluators with criterion specific scores covering correctness, richness, coherence, cohesion, and task alignment enriched with detailed feedback and error examples. We fine-tune open-source models, including RoBERTa-EusCrawl and Latxa 8B/70B, for both scoring and explanation generation. Our experiments show that encoder models remain highly reliable for AES, while supervised fine-tuning (SFT) of Latxa significantly enhances performance, surpassing state-of-the-art (SoTA) closed-source systems such as GPT...

📄 Full Content

The assessment of advanced second-language writing remains a complex and resource-intensive task, particularly at CEFR Level C1, where proficiency is characterized by near-native use of language, flexibility in style and register, and coherence across extended discourse. While traditional human-based evaluation offers rich pedagogical insights, it is inherently time-consuming, costly, and susceptible to inter-rater variability, thus limiting its scalability in large-scale or continuous learning contexts (Arriola et al., 2023). Automatic Essay Scoring (AES) offers a promising alternative by enabling consistent

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