Reducing morphosyntactic errors of students’ English writing through AIpowered language learning tools

Morphosyntactic errors persist as a challenge for English as a Foreign Language (EFL) students, particularly in academic writing purposes in the English Department of UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan or called also known as UIN SYAHADA Padangsidimpuan. The errors are including incorrect verb tense usage, subject–verb disagreement, and misapplication of word order, can significantly hinder clarity and communicative effectiveness. This study aims to investigate the effectiveness of AI-powered language learning tools in reducing morphosyntactic errors in English writing. The research applies a quasi-experimental design involving Indonesian university students enrolled in an advanced writing course of Semester VII. Participants in the experimental group received targeted feedback generated by AI tools integrated with morphosyntactic analysis, while the control group received conventional teacher feedback. Pre-test and post-test writing tasks were analysed using a morphosyntactic error taxonomy to measure improvement. Results indicate that AI-assisted feedback not only reduces the frequency of morphosyntactic errors but also develops students’ awareness of grammatical accuracy. The researcher got the findings, which suggest that integrating AI-powered tools into EFL writing instruction can serve as an effective supplement to traditional feedback methods, offering personalized, immediate, and consistent error correction. Implications for language pedagogy and further research are discussed.