Using Automated Writing Evaluation to Improve EFL Students’ IELTS Writing: Effects Across Proficiency Levels and Learning Behaviours

Main Article Content

Napat Jitpaisarnwattana
Nick Saville

Abstract

This study examines the impact of Write & Improve, an Automated Writing Evaluation (AWE) tool, on Thai university students' performance in IELTS writing. Additionally, it explores learning behaviors that may contribute to writing development. Employing a quantitative approach, the study gathered data from 53 students enrolled in an English Preparation for Standardized Tests course at a Thai public university. The effectiveness of the AWE tool was assessed through a comparative analysis of pre- and post-test scores, while learning behaviors were examined using program-generated learning logs. The findings indicate that the Write & Improve intervention led to significant improvements in students’ post-test writing performance across both writing tasks and all assessment criteria, with the most notable gains observed in grammatical range and accuracy. The most substantial progress was evident among intermediate-level students (IELTS Band 5.5 and below). Furthermore, learning analytics suggests that students who engaged in a greater number of writing tasks demonstrated stronger post-test performance. The study concludes that while AWE tools can facilitate improvements in specific writing components, their integration should be guided by well-defined pedagogical objectives, with teacher involvement remaining essential in the feedback process.

Article Details

How to Cite
Jitpaisarnwattana, N., & Saville , N. (2026). Using Automated Writing Evaluation to Improve EFL Students’ IELTS Writing: Effects Across Proficiency Levels and Learning Behaviours. LEARN Journal: Language Education and Acquisition Research Network, 19(2), 265–296. https://doi.org/10.70730/XYWK3308
Section
Research Articles
Author Biographies

Napat Jitpaisarnwattana, Silpakorn University, Thailand

A lecturer of English and Computer-assisted Language Learning at Silpakorn University, Thailand. He has recently finished a Master of Studies focusing on Machine Learning and Automated Language Assessment at Homerton College, University of Cambridge. He is an associate editor of rEFLections Journal and an associate editor of the International Journal of Southeast Asian Media Studies. His research interests include LMOOCs, the Internet of things, digital wellbeing, digital literacies, learning analytics, machine learning, natural language processing and AI in language education.

Nick Saville , Cambridge University Press and Assessment

Director of Research and Thought Leadership, Cambridge Assessment English. His research interests are language proficiency scales and descriptors in the context of assessment (e.g. the CEFR and English Profile Programmed); learning-oriented assessment, including socio-cognitive models of learning and assessment, pedagogical grammars of English and interfaces with SLA; language and migration, including multilingualism and the impact of language assessment in social contexts.

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