@inproceedings{nebhi-etal-2025-end,
    title = "End-to-End Automated Item Generation and Scoring for Adaptive {E}nglish Writing Assessment with Large Language Models",
    author = "Nebhi, Kamel  and
      Panesar, Amrita  and
      Bantilan, Hans",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bea-1.73/",
    pages = "968--977",
    ISBN = "979-8-89176-270-1",
    abstract = "Automated item generation (AIG) is a key enabler for scaling language proficiency assessments. We present an end-to-end methodology for automated generation, annotation, and integration of adaptive writing items for the EF Standard English Test (EFSET), leveraging recent advances in large language models (LLMs). Our pipeline uses few-shot prompting with state-of-the-art LLMs to generate diverse, proficiency-aligned prompts, rigorously validated by expert reviewers. For robust scoring, we construct a synthetic response dataset via majority-vote LLM annotation and fine-tune a LLaMA 3.1 (8B) model. For each writing item, a range of proficiency-aligned synthetic responses, designed to emulate authentic student work, are produced for model training and evaluation. These results demonstrate substantial gains in scalability and validity, offering a replicable framework for next-generation adaptive language testing."
}