End-to-End Automated Item Generation and Scoring for Adaptive English Writing Assessment with Large Language Models

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.