arXiv:2609.23573v1 Announce Type: new
Abstract: Synthetic essays can help reduce dependence on human-written data in Automated Essay Scoring (AES). However, they often lack realistic errors, limiting...
By Duy Anh Nguyen
arXiv:2606. 10327v1 Announce Type: cross Abstract: Automated Essay Scoring (AES) systems must judge interdependent discourse elements (e.
By Ali Keramati, Mark Warschauer
arXiv:2607. 15829v1 Announce Type: cross Abstract: Automated essay scoring (AES) enables scalable assessment and timely feedback but remains challenged by transformer input-length limitations, which can cause information loss when processing long essays.
By Haowei Hua
arXiv:2602. 01747v2 Announce Type: replace-cross Abstract: Automated Essay Scoring (AES) plays a crucial role in education by providing scalable and efficient assessment tools.
By Hongseok Choi, Serynn Kim, Wencke Liermann, Jin Seong, Jin-Xia Huang
The study examines whether domain‑adaptive continued pretraining (DAPT) on a learner‑writing corpus (EFCAMDAT) can enhance transformer‑based automated essay scoring (AES) for English proficiency tests. Researchers applied DAPT to BERT, RoBERTa, and DistilBERT and compared the adapted models with their original checkpoints on the FCE and IELTS datasets, evaluating both in‑domain scoring and few‑shot cross‑dataset transfer. Results show that full‑corpus DAPT yields mixed effects, while proficiency‑specific DAPT often outperforms full‑corpus DAPT and sometimes even the non‑adapted baseline, though benefits vary by proficiency composition and encoder architecture and do not consistently transfer across tests.
By Duy Anh Nguyen
The paper "Limits of LLM Text Detectors in Education" argues that existing LLM‑generated text detectors assume a binary human/LLM distinction, which fails to capture realistic student‑AI collaboration. It introduces a contribution‑aware evaluation framework with eight student contribution levels and presents GEDE, a benchmark of over 900 human‑written and 12,500 generated essays across 886 tasks. Using GEDE, the authors evaluate four detection methods and find that most detectors perform poorly on intermediate contribution levels, especially LLM‑assisted revisions, raising concerns about false accusations.
By Lukas Gehring, Benjamin Paa{\ss}en