Generative AI as a Writing Feedback Tool in Arabic Language Learning: A Mixed-Methods Study in Indonesian Islamic Higher Education
DOI:
https://doi.org/10.54471/c0jzr218Keywords:
generative artificial intelligence, Arabic writing, automated written feedback, mixed methods, Islamic higher educationAbstract
Background and research gap – Generative artificial intelligence (GenAI) is rapidly entering second-language writing classrooms, yet empirical evidence comes almost entirely from English. Its reliability and pedagogical value for Arabic, a morphologically rich language taught in Indonesia as both a religious and an academic language, remain underexplored. Purpose – This study examines whether GenAI-generated feedback improves students’ Arabic writing performance, how linguistically accurate that feedback is, and how students perceive and engage with it. Design/methodology/approach – A convergent parallel mixed-methods design combined a quasi-experiment with 62 Arabic Language Education students (experimental n = 31; control n = 31) over eight weeks, expert accuracy coding of 1,248 feedback points, a perception questionnaire, and semi-structured interviews with 12 students. Key Findings – The experimental group outperformed the control group on post-test writing scores (F(1, 59) = 14.62, p < .001, partial η² = .20). GenAI feedback was 81.3% accurate overall but weakest on iʿrāb (case endings). Students valued its immediacy and low-anxiety tone while adopting “trust but verify” strategies. Research limitations/implications – The single-institution sample and short intervention limit generalisability; GenAI feedback should complement, not replace, lecturer feedback. Originality/value – The study provides early mixed-methods evidence on GenAI feedback for Arabic writing and proposes a lecturer-mediated feedback model for Islamic higher education.
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