Grammatical Error Patterns in ChatGPT-Generated Modern Standard Arabic Texts: A Linguistic Analysis of Recurrent Patterns

Abstract

Despite significant advances in AI language models, Modern Standard Arabic (MSA) remains a linguistically complex domain in which apparent fluency often masks deeper grammatical instability. This study investigates recurrent grammatical error patterns in ChatGPT-generated Arabic texts, focusing on how these patterns reflect underlying morpho-syntactic challenges and the constraints of probabilistic language generation. Adopting a qualitative, pattern-oriented analytical framework, the study draws on online focus group discussions with secondary-level Arabic teachers, who served as expert linguistic evaluators. Participants collaboratively examined a set of AI-generated texts to identify and interpret systematic grammatical deviations across five key domains: agreement, inflection and case marking, sentence structure, prepositions and transitivity, and cross-linguistic influence. The findings indicate that grammatical errors in AI-generated Arabic are not random but occur as recurring, structured patterns, particularly in contexts involving long-distance dependencies and morphologically complex constructions. These patterns suggest a reliance on surface-level fluency at the expense of deeper grammatical coherence, reflecting limitations in maintaining consistent morpho-syntactic relationships. This study contributes by identifying and characterizing systematic grammatical patterns in AI-generated MSA as interpreted through expert linguistic judgment, offering a qualitative perspective that complements existing quantitative approaches and advances understanding of how large language models engage with morphologically rich languages.

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