Artificial Intelligence for Arabic Natural Language Processing: A Scoping Review of Applications, Resources, Evaluation Practices, and Evidence Gaps
Keywords:
Arabic NLP, artificial intelligence, Arabic dialects, language resourcesAbstract
Arabic natural language processing (NLP) increasingly uses transformer models and generative artificial intelligence, yet linguistic variety and evaluation heterogeneity challenge interpretation. This scoping-review manuscript maps applications, datasets, assessment practices and gaps across Arabic text studies from 2021 to October 2026. A Population–Concept–Context framework and PRISMA-ScR informed the planned search and charting approach. An exploratory collection of 23 original journal articles documents dialect identification, classification, translation, question answering, summarization, error correction and culturally situated evaluation. Across these studies, model scores depend strongly on dialect, genre, annotation protocols and benchmark design. The principal implication is the need for dialect-disaggregated tests, transparent dataset provenance, stronger external validation and qualified human assessment. Comprehensive database retrieval and independently documented screening remain necessary before this evidence map can be reported as a completed scoping review.
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