Using Corpus-Based Approaches to Teach Arabic as a Foreign Language: A Sketch Engine–Driven Model

Background Corpus-based Arabic language teaching is increasingly urgent as digital learning demands authentic, reproducible evidence of usage. Yet Arabic as a foreign language remains dominated by decontextualized vocabulary and prescriptive rules. This study addresses the underexplored pedagogical integration of Sketch Engine into Arabic vocabulary instruction through naturally occurring discourse. Methods Guided by data-driven learning and corpus linguistics, particularly the principles of Johns, Sinclair, McEnery, and Boulton, the study employs a qualitative corpus-based design. Sketch Engine modules—Concordance, Word Sketch, Thesaurus, Wordlist, and Keywords—are applied to authentic Arabic corpora to investigate contextual meaning, collocations, synonyms, frequency, morphology, and register variation. Results Corpus-based analysis reveals that Arabic lexical meaning changes substantially across everyday, journalistic, legal, academic, and technical discourse. Recurrent collocations encode cultural values; apparent synonyms display distinct contextual distributions; and root derivatives perform register-specific semantic and rhetorical functions. Sketch Engine consequently strengthens learners’ lexical, morphological, collocational, and discourse awareness while supporting more natural Arabic production. Conclusions A Sketch Engine-driven model can transform Arabic as a foreign language teaching from memorization into evidence-based linguistic discovery. Future research should experimentally evaluate proficiency gains, develop level-sensitive corpus tasks, construct multilingual learner corpora, and openly share datasets and teaching workflows to ensure replication and cross-contextual adaptation.