An Examination of the Relationship Between Feedback Quality and Student Self-Regulation in Generative AI-Supported English Writing Instruction at Tertiary Level in Middle Asia

Abstract

This study examines the relationship between feedback quality and student self-regulation in generative AI-assisted English writing instruction at tertiary level in Uzbekistan. In contemporary writing pedagogy, feedback is considered not only an element supporting textual revision, but also a fundamental formative mechanism shaping student’s capacity to plan, monitor, and improve their writing processes. In this context, the pedagogical value of generative AI becomes more meaningful when the feedback it produces is clear, applicable, and capable of guiding students to interact with their texts in a reflective way in Middle Asian universities.
The study is theoretically framed through the interaction between feedback quality and self-regulation in AI-assisted writing environments at tertiary level in Middle Asia. It is argued that high-quality feedback can strengthen student’s attention regulation, revision decisions, and strategy use by making the writing process more visible and manageable. Accordingly, the study concludes that generative AI-assisted writing instruction can contribute more effectively to student development when feedback functions as a structured guide for autonomous and sustainable participation in writing tasks in Middle Asia at tertiary level.