A Longitudinal Mixed-Methods Study on the Complementary Roles of AI Tools and Teacher Expertise in L2 Writing Instruction

Abstract

Artificial intelligence (AI) tools are increasingly used in second language (L2) writing instruction, yet empirical research comparing their longitudinal effects against human feedback remains limited. This longitudinal, mixed-methods quasi-experimental study investigated the impact of AI-based feedback versus teacher-mediated feedback on the grammatical accuracy, syntactic complexity, and writing quality of 60 first-year English Language Teaching students. Over an 8-week intervention, developmental trajectories were analyzed using Linear Mixed-Effects Models. Results revealed a significant Group & times; Time interaction favoring the AI-Feedback group for grammatical accuracy, which exhibited a significantly steeper rate of error reduction. Conversely, the Teacher-Feedback group demonstrated superior growth in discourse-level writing quality (p = .036). Syntactic complexity developed comparably across both conditions (p = .41), suggesting that gains in linguistic maturity were driven by task repetition and cognitive engagement rather than the specific feedback modality. Qualitative findings highlighted a critical tension: while AI tools significantly reduced writing anxiety and facilitated immediate iterative revision, participants expressed skepticism regarding the contextual reliability of automated suggestions, retaining a strong preference for human mediation to address higher-order concerns. These findings advocate for a blended pedagogical approach, where L2 learners leverage AI for surface-level precision while relying on human feedback to scaffold rhetorical and conceptual development.