ARTIFICIAL INTELLIGENCE AND THE DEVELOPMENT OF TRANSLATION COMPETENCE IN ARAB EFL LEARNERS: A SYSTEMATIC REVIEW OF THE LITERATURE

Abstract

The rapid diffusion of neural machine translation and large language models has altered the conditions under which translation is learned and practiced, raising questions about how these technologies interact with the development of translation competence among learners of English as a foreign language (EFL) in the Arab world. This systematic review synthesizes theoretical and empirical scholarship in order to establish what is currently known about that interaction and where the evidence remains thin. Following a structured search of major databases, the review first reconstructs the foundational theories that underpin the construct of translation competence, including multicomponent models, process-oriented accounts of competence acquisition, social constructivist approaches to translator education, and more recent technology-oriented extensions such as machine translation literacy and post-editing competence. It then organizes empirical studies published between 2020 and 2026 into six themes: the quality and reliability of artificial intelligence output in the English–Arabic pair; learner perceptions, attitudes, and acceptance; instructional interventions and measured gains in performance; post-editing as a pedagogical route into competence; autonomy, over-reliance, and academic integrity; and curricular provision and trainer readiness in Arab programmes. The synthesis indicates broad agreement that artificial intelligence tools raise surface fluency and productivity while performing unevenly on culture-bound, idiomatic, and register-sensitive material, and that pedagogical mediation rather than tool access determines whether learners gain competence. Persistent gaps include the scarcity of longitudinal and experimental designs, the dominance of self-report instruments, the concentration of research in a small number of Arab national contexts, and the absence of validated instruments for measuring technology-inclusive translation competence in Arabic–English settings.