Generative Artificial Intelligence And Reducing The Cognitive Load In Language Learning: A Study In The Algerian Educational Environment

Abstract

This study examined the cognitive challenges faced by Algerian learners and teachers within a highly complex linguistic environment, where Classical Arabic, Algerian dialect, Amazigh, French, and English intersect, generating a compounded cognitive load that burdens mental processing. The study drew on Cognitive Load Theory (Sweller, 1988) and the theory of limited resources in second language processing (VanPatten, 2004). It addressed the following central question: How can generative artificial intelligence redistribute and relieve the linguistic cognitive load in the Algerian educational context? Three main axes were examined: the patterns of cognitive load resulting from multilingualism in Algeria; the potential of generative AI as a linguistic cognitive mediator; and the proposal of an integrated theoretical framework linking cognitive psychology, applied linguistics, and artificial intelligence. The study concluded with theoretical and applied recommendations aimed at building inclusive digital learning environments that account for the linguistic specificity of the Algerian learner