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Artificial intelligence (AI) has made its mark on every facet of our everyday existence, leaving no aspect untouched. In the realm of Machine Translation, Google Neural Machine Translation (GNMT) stands out among the various tools, gaining significant recognition. As has been already witnessed, GNMT, among other machine translation tools, continuously evolves to facilitate even better and more efficient learning and language teaching, making the work easier for teachers and learners. The acceptability of the concept has improved, whereas it would have been heavily criticized in the past.
The increased usage of internet-connected devices among students has sparked a growing interest in researching the implications of their use, particularly with tools like GNMT, which is the most widely used type of machine translation. In addition to its popularity among students, Google Translate has consistently advanced its technology, including implementing GNMT in 2016 (Bahri & Mahadi, 2020). It continues to undergo upgrades based on user query data, further enhancing its capabilities. This shift from utilizing Statistical Machine Translation (SMT) to using artificial neural networks in the recently discovered Neural Machine Translation (NMT) has improved translation accuracy, speed, and efficiency (Poibeau, 2017). In multiple studies, MT has been described as using specific software to translate text between various languages as desired (Qun & Xiaojun, 2015). Compared to the initially used technology, the most recent NMT has been verified to produce the desired output more efficiently and faster. Furthermore, it has demonstrated greater accuracy in translation, supported by sufficient databases (Bahri & Mahadi, 2020; Poibeau, 2017).
There are objections to the use of MT for several reasons. The first and most common assumption suggests that students will not gain as much knowledge or proficiency in a language when using MT. For example, in mathematics, teachers should not introduce their students to using calculators before they can master the basic concepts in calculations. A student would not derive any educational value if they merely used a search engine to find and write down the answer to a math problem without actively working on it, which applies to learning translation practice. There is a difference in learning when one writes a sentence and looks up the translation of the same sentence compared to when one produces the sentence and then translates it. Instructors in different languages often look for the ability to write in the target language and assess students' ability to translate from one language into another.
This paper aims to explore mindful approaches to employing GNMT effectively to improve students’ abilities in translation. It emphasizes its usefulness for students by exploring its potential in learning the practice of translation. This research highlights the strengths and limitations of using MT in translation-learning classrooms and attempts to answer the following questions:
- 1.
How should students of translation utilize GNMT effectively in translation-learning classroom?
- 2.
What outcomes can be anticipated from employing different approaches to learning translation through GNMT?
Two hypotheses can be derived from the first research question; each of which refers to a different translation approach. The first relates to the conventional approach employed by students that includes pre-editing and post-editing, whereas the second relates to a comprehensive meticulous approach in which students employ human translation and make multi-edits in applying GNMT to develop their performance:
- 1.
H1: μPreB ≠ μPostB (There is significant difference between pre- and post- applying a conventional approach to learning translation through GNMT among Group A).
- 2.
H2: μPreA ≠ μPostA (There is significant difference between pre- and post- applying a comprehensive meticulous approach to learning translation through GNMT among Group B).
For the second research question, it is hypothesized that both groups will show improvements in translation abilities, but Group B will exhibit a more significant improvement compared to Group A: