FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English Writing

• URL: http://arxiv.org/abs/2511.22883v1
• Date: Fri, 28 Nov 2025 05:17:45 GMT
• Title: FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English Writing
• Authors: Jingheng Ye, Shen Wang, Jiaqi Chen, Hebin Wang, Deqing Zou, Yanyu Zhu, Jiwei Tang, Hai-Tao Zheng, Ruitong Liu, Haoyang Li, Yanfeng Wang, Qingsong Wen,
• Abstract summary: We present the Fine-grained Error ANalysis for English learners (FEANEL) Benchmark.<n>The benchmark comprises 1,000 essays written by elementary and secondary school students.<n>Each error is annotated by language education experts and categorized by type, severity, and explanatory feedback, using a part-of-speech-based taxonomy they co-developed.
• Score: 68.23874413455594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have transformed artificial intelligence, offering profound opportunities for educational applications. However, their ability to provide fine-grained educational feedback for K-12 English writing remains underexplored. In this paper, we challenge the error analysis and pedagogical skills of LLMs by introducing the problem of Fine-grained Error Analysis for English Learners and present the Fine-grained Error ANalysis for English Learners (FEANEL) Benchmark. The benchmark comprises 1,000 essays written by elementary and secondary school students, and a well-developed English writing error taxonomy. Each error is annotated by language education experts and categorized by type, severity, and explanatory feedback, using a part-of-speech-based taxonomy they co-developed. We evaluate state-of-the-art LLMs on the FEANEL Benchmark to explore their error analysis and pedagogical abilities. Experimental results reveal significant gaps in current LLMs' ability to perform fine-grained error analysis, highlighting the need for advancements in particular methods for educational applications.

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