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The integration of artificial intelligence (AI) in the teaching of English as a Foreign Language (EFL) is on the rise alongside technological progress. This implementation is founded on various contemporary theories that have become central in academia, particularly regarding non-native speakers. These theories encompass sociocultural approaches, connectivism, and adaptive learning, which work in conjunction with AI’s capacity to tailor learning experiences and enhance language engagement.

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The integration of artificial intelligence (AI) in the teaching of English as a Foreign Language (EFL) is on the rise alongside technological progress. This implementation is founded on various contemporary theories that have become central in academia, particularly regarding non-native speakers. These theories encompass sociocultural approaches, connectivism, and adaptive learning, which work in conjunction with AI’s capacity to tailor learning experiences and enhance language engagement.

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This dataset was created and utilized for preparing an article submitted to the IEEE Transactions on Evolutionary Computtations. Based on critical review and experiment, the  article intends to shed some light on the  way crossover-mutation intraction balannce in GA could be comprehended in the view tthat current literature is short of providing generalized rule that guides determination of tha balance of the two operators across domains of problems.

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