Work

Multi-component student writing profiles for expert-aligned automated evaluation of English learner essays.

Year: 2026

Type: conference-paper

Abstract: Automated Writing Evaluation (AWE) platforms have become common, but a significant gap remains between automated assessment and expert human feedback.We address this gap by introducing a supervised learning method that uses a multi-component student writing profile (comprising estimated CEFR level, grammatical error rates, and vocabulary distribution) to align AI scoring with expert human judgements.In the context of an online essay-writing platform for second language learners of English, our model achieves a 36% reduction in RMSE for holistic essay scoring and an 84% improvement in similarity to human-expert analysis of grammatical errors compared to automarker scores (26% and 57% improvement from the best-performing comparable earlier work, by Zaidi et al., 2019.Furthermore, we demonstrate that the model can predict a student's profile for a final-version essay from earlier drafts and that predictions generalise to a subsequent task, offering new possibilities for automated curriculum planning.Finally, we introduce a visualisation tool that provides educators with clear expert-aligned longitudinal views of student development.

Source: Workshop on Innovative Use of NLP for Building Educational Applications

DOI: 10.18653/v1/2026.bea-1.39

Authors: Russell Moore, Andrew Caines, Paula Buttery

Institution: University of Cambridge

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Funder: University of Cambridge