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...
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.
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.
arXiv:2606. 20152v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have substantially transformed Automated Essay Scoring (AES), yet the internal mechanisms underlying LLM-based scoring remain poorly understood.
The paper introduces SWIM, a task that frames student writing simulation as proficiency‑conditioned essay generation. It evaluates prompting, supervised fine‑tuning, and reinforcement learning for aligning generated essays with student proficiency profiles, using automated essay scoring as a metric. Results show that prompting alone offers limited control, while supervised fine‑tuning and reinforcement learning significantly improve alignment across content, lexical, grammatical, and organizational traits, though low‑proficiency writing remains difficult to replicate.
arXiv:2606. 10327v1 Announce Type: cross Abstract: Automated Essay Scoring (AES) systems must judge interdependent discourse elements (e.
The paper introduces a cost‑aware framework that treats each prompt type as an arm in a multi‑armed bandit controller, enabling adaptive selection of optimal prompting strategies during inference for automated essay scoring. Experiments on IELTS Writing Task 2 essays demonstrate that this bandit-driven approach achieves comparable scoring accuracy to exhaustive grid search while reducing LLM calls by 78.4%. The study also presents the first cost‑reliability learning curves for essay scoring, offering actionable insights for educational technology platforms balancing operational costs against assessment validity.
arXiv:2607. 14524v1 Announce Type: new Abstract: This study presents WrAFT, a Writing Assessment and Feedback Tool, that delivers both accurate and reliable scores and effective comprehensive feedback to argumentative essays.
Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG). However, existing studies rely primarily on prompt engineering or supervised fine-tuning, while systematic research on reinforcement learning (RL) post-training and automated evaluation of feedback quality remains limited.
arXiv:2607. 19219v1 Announce Type: cross Abstract: Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG).
The paper introduces a unified training objective that blends focal loss with supervised contrastive learning to improve automatic CEFR level assessment. This approach addresses class imbalance and ambiguous level boundaries by emphasizing hard examples and learning discriminative representations. Experiments on sentence- and document-level CEFR assessment in both monolingual and multilingual contexts show that the method outperforms strong baselines and sets new state‑of‑the‑art results on multiple benchmarks.
arXiv:2606. 24973v1 Announce Type: cross Abstract: We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning 328 questions across five subjects and including handwritten work.