AI-Driven Arabic Grammatical Error Correction for Inshā’ Writing: A Systematic Review of Current Models and Pedagogical Directions - Abdurahman Abdurahman; Siti Nurul Azkiyah; Muhbib Abdul Wahab; Maswani Maswani (Online Learning In Educational Research (OLER), 2026)
Artificial intelligence (AI) has expanded opportunities for supporting Arabic writing through automated grammatical error correction (GEC). However, existing research remains fragmented, with limited synthesis connecting learner error patterns, AI-based correction models, and their pedagogical implications for Inshā’ instruction. This study systematically reviews AI-based Arabic GEC research to identify dominant nahwu–ṣarf error patterns, evaluate the evolution of AI correction models, and examine their educational implications for non-native Arabic learners. Following the PRISMA framework, eleven studies published between 2020 and 2025 were synthesized using qualitative thematic analysis and VOSviewer-assisted keyword mapping. The review identifies recurrent learner difficulties in agreement, definiteness, morphological ambiguity, syntactic dependencies, orthographic variation, and diacritical accuracy. Arabic GEC has progressed from rule-based and hybrid approaches to Transformer- and large language model (LLM)-based systems that provide more context-sensitive corrections. Nevertheless, current models remain constrained by challenges related to i‘rāb, learner-specific error profiles, explainable grammatical feedback, and limited annotated corpora. The review proposes a human-in-the-loop pedagogical framework integrating AI-generated feedback with teacher validation to foster formative assessment, learner autonomy, and reflective revision in Arabic writing instruction