Enhancing Sustainable English Writing Instruction Through a Generative AI-Based Virtual Teacher Within a Co-Regulated Learning Framework

Abstract

English writing proficiency is pivotal to sustainable academic success and employability. In Chinese higher education, however, conventional instruction often constrains students’ self-regulation and access to individualized feedback. Drawing on self-regulated learning (SRL) and co-regulated learning (CoRL), this study investigates whether a CoRL-guided generative AI virtual teacher (CoRL-VT), designed as a “more capable other,” is associated with enhanced undergraduate writing outcomes relative to standard AI support. Using a 12-week quasi-experimental design with two intact classes (N = 61) in Anhui, China, we compared a control condition (standard AI) with an intervention (CoRL-VT). Writing proficiency was assessed via IELTS Writing Task 2 at pre- and post-test; three certified examiners scored all scripts with strong agreement (ICC = 0.87). Analyses adjusting for baseline yielded an estimated group difference favoring CoRL-VT. Teacher interview testimony aligned with the quantitative pattern, noting clearer macro-organization, richer lexical choices, and more teacherly formative feedback among CoRL-VT students. Taken together, these findings offer exploratory, descriptive evidence consistent with the potential of structured, CoRL-informed AI scaffolding in sustainable writing pedagogy and outline design principles for replicable CoRL-VT implementations in resource-conscious contexts.