The Use of Machine Translation as a Tool for Teaching Translation at University
TL;DR
The study concludes that machine translation should be used as an auxiliary tool under teacher supervision and combines traditional translation training with digital literacy, professional responsibility, and systematic post-editing skills.
Abstract
The article examines the didactic potential of machine translation in university translator training. The study aims to identify learning tasks in which automated systems support the development of translation competence without replacing students’ independent analytical work. The research material includes publications on neural machine translation, digital competence, post-editing, methodological recommendations, and ISO 18587:2017. The methods comprise literature analysis, comparison of pedagogical approaches, classification of recurrent errors, and modeling of classroom assignments. The findings show that comparing a source text with machine-generated output helps students identify semantic, terminological, stylistic, and culture-specific errors. Post-editing develops fact-checking, terminology management, and the ability to assess whether a translation is suitable for a particular communicative purpose. Unsupervised reliance on online services, however, may weaken independent decision-making and encourage uncritical trust in automatically generated text. A staged classroom procedure is proposed: an initial translation without digital assistance, production of a machine-translated version, classification of discrepancies, reasoned editing, and final reflection. For language pairs involving Kyrgyz, special attention should be paid to culture-bound vocabulary and the limited availability of digital corpora. The study concludes that machine translation should be used as an auxiliary tool under teacher supervision. This approach combines traditional translation training with digital literacy, professional responsibility, and systematic post-editing skills.