{"ID":2835338,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.22883","arxiv_id":"2511.22883","title":"FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English Writing","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.","short_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 o...","url_abs":"https://arxiv.org/abs/2511.22883","url_pdf":"https://arxiv.org/pdf/2511.22883v1","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\"]","published":"2025-11-28T05:17:45Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}