A Dynamic Written Corrective Feedback Framework Integrating AI Agent Delivery for Structured and Iterative Essay Support

Summary

This study benchmarks STEP-DWCF-R (Structured, Tiered, Evidence-driven Process for Dynamic Written Corrective Feedback with Robotic AI Agent) for improving IELTS writing performance through multi-round AI and teacher-supported revisions.

Abstract

Automated writing feedback systems are prevalent, yet most deliver static, fragmented comments that provide limited scaffolding for revision. This study evaluates the STEP-DWCF-R framework (Structured, Tiered, Evidence-driven Process for Dynamic Written Corrective Feedback with Robotic AI Agent), in which AI-generated feedback, moderated by a teacher, is delivered via a robotic AI agent across multiple iterative rounds within a one-week task cycle. In an eight-week quasi-experimental trial, 32 EFL learners were randomized to either traditional written corrective feedback (one round per task) or STEP-DWCF-R. Both groups completed IELTS Task 2 essays at baseline and post-test, which were anonymized, randomized, and scored by two independent raters (ICC = 0.86–0.93). Linear mixed-effects models demonstrated that the STEP-DWCF-R group exhibited significantly greater gains in overall band score (Δ = 1.03 vs. 0.31 bands) and across all four analytic dimensions, with the largest improvement observed in Coherence and Cohesion. Process data indicated that STEP-DWCF-R learners completed an average of 2.26 revision rounds per task, with error counts decreasing linearly across rounds. These findings suggest that the integrated STEP-DWCF-R framework, encompassing AI-generated feedback, teacher moderation, and iterative robotic AI agent delivery, is associated with greater IELTS writing improvement than traditional single-round feedback, pointing to practical applications for AI-enhanced dynamic feedback in EFL contexts.

Introduction

Written corrective feedback (WCF) remains central to L2 writing pedagogy. Evidence shows that comprehensive WCF improves learners’ accuracy over time and, when aligned with classroom practice, can coexist with focused approaches that are feasible in authentic contexts^1,2,3. Recent classroom studies show durable gains in accuracy and fluency from sustained, comprehensive WCF. Research on feedback scope cautions that teachers should target forms strategically rather than mark everything^4,5,

Protocol

This protocol was approved by the Ethics Committee of the School of Primary Education, Shangrao Preschool Education College. All participants were adults (≥18 years) and provided written informed consent prior to the study.

1. Study design

NOTE: Due to the constraints of the available EFL classroom (total enrollment N = 32), participants were randomized into two groups of 16 each. This sample size, while modest, was determined to be sufficient for a pilot investigation given the within-subject pre-post design and expected large effect sizes based on prior DWCF studies¹⁷

Results

Experiments and Analysis

All 32 learners provided baseline data. Post-test data were available for 16 learners in each group (WCF and STEP-DWCF-R; Figure 2). The study timeline and measures are presented in Figure 3, and the end-to-end feedback workflow is illustrated in Figure 4. Experimental setup and implementation parameters for our AI system are shown in Table 1. Ana.......

Discussion

This study compared STEP-DWCF-R, a multi-component dynamic written corrective feedback framework with a robotic AI agent, with single-round WCF in an eight-week IELTS writing course. STEP-DWCF-R yielded larger pre–post gains in overall band and across TR, CC, LR, and GRA. Learners under STEP-DWCF-R also completed more revision rounds and showed faster error reduction, with models estimating a decline of about 4.1 errors per round. The greatest improvement was in coherence and cohesion (ΔΔ = 0.85), indicat.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Flask (Web Framework)	Pallets Projects	https://flask.palletsprojects.com	Version 2.1; used for robotic AI agent web interface
GPT-5 API	OpenAI	https://platform.openai.com	Model gpt-5, temperature=0.7; for structured JSON feedback generation
Jinja2	Pallets Projects	https://jinja.palletsprojects.com	Server-rendered templates for web interface
Python	Python Software Foundation	https://www.python.org	Version 3.10; backend scripting
Windows 11	Microsoft	https://www.microsoft.com/windows	Operating system for AI feedback system