International Journal of
Information and Education Technology

Abstract

In the domain of Technology-Enhanced Language Learning (TELL), this mixed-methods study explores the efficacy of the iWrite platform in enhancing English writing among Chinese junior secondary students (N = 56), grounded in Formative Feedback Theory (FFT). Key findings revealed quantitative results showing the experimental group achieved a +10.16% improvement in CEFR-aligned writing scores, compared to a +0.74% gain in the control group (Δ = +9.42%, d = 1.30), with three significant pathways: 1) direct impact (48.4% total effect, β = 0.44, p < 0.05); 2) behavior-driven pathway (36.3% total effect, β = 0.33, p < 0.05); 3) chained feedback pathway (18.7% total effect, β = 0.17, p < 0.05). Qualitative insights (n = 28) indicated 85.7% of students valued self-regulation tools, 82.1% cited the importance of instant feedback, and 78.6% linked reflection to improved logical thinking. Critically, iWrite significantly enhanced foundational skills (CEFR A2–B1 vocabulary/grammar), with mediation pathways validating this effect (R² = 0.84). However, its limited impact on critical analysis revealed a competency gap that requires teacher scaffolding, necessitating a hybrid AI-human model. Ultimately, this research provides a scalable model for AI-driven, FFT-based writing competency pedagogy.