Comparing teacher and AI-generated feedback in the writing classroom: Experimental results from secondary school classrooms

Writing proficiency is an essential skill for secondary school students and can be supported through high-quality feedback. However, providing individualized feedback on student writing is timeintensive, which often limits its availability in classroom practice. Recent advances in large language models (LLMs) have raised interest in automated feedback as a potential way to scale and supplement teacher practice, yet evidence of its effectiveness relative to teacher feedback in authentic classroom settings remains limited. In this study, we examined the effects of LLM-generated feedback on students' essay revisions, subsequent writing performance, and feedback perceptions in an authentic English-as-a-foreignlanguage context in secondary schools (N = 391). We compared teacher-written feedback against both delayed and immediate LLMgenerated feedback. We further conducted Bayesian analyses to characterize the magnitude and uncertainty of differences between feedback conditions. Together, these findings point to a trade-off between small performance differences associated with teacher feedback that were neither statistically or practically significant. At the same time, results showed more positive perceptions of usefulness and motivation associated with immediate feedback, which only LLMs can provide at scale. These findings suggest that the primary educational value of LLM-based feedback lies less in outperforming teachers and more in redistributing instructional effort. When used to consistently support revision and short-term learning, AI feedback may free teachers to focus on higher-order instructional planning, targeted scaffolding, and pedagogical interaction, highlighting the potential of hybrid feedback systems in writing instruction.