Neural machine translation in foreign language teaching and learning: a systematic review

Nowadays, hardly anyone working in the field of foreign language teaching and learning can imagine life without machine translation (MT) tools. Thanks to the rapid development of artificial intelligence, MT now most widely assumes a new form, the so-called Neural Machine Translation (NMT), which offers the potential for a wide application in foreign language learning (FLL). Therefore, the purpose of this review study is to explore different approaches to the efficient implementation of NMT into FLL and provide specific pedagogical implications for best practices. The PRISMA methodology for systematic reviews and meta-analyses was strictly followed. The search was conducted in two well-established databases, specifically Scopus and Web of Science, to generate sufficient data from research articles for further analysis. The findings of this systematic review indicate that NMT is an efficient tool for developing both productive (speaking and writing) and receptive (reading and listening) language skills, including mediation skills, which are relevant for translation. Moreover, the results show that NMT tools are especially suitable for advanced learners of L2, whose higher proficiency level enables them to critically reflect on the output of NMT texts more than beginners or lower-intermediate learners. Thus, the findings of this review study reveal that NMT has valuable implications for L2 pedagogy since it can serve as a very powerful online reference tool for FLL provided that teachers introduce students to its benefits but also limitations by implementing various teaching approaches.