The Dynamics of Translation Teaching in the Era of Machine Translation: Challenges, Ethics, and Learning Strategies
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Abstract
The development of automatic translation (machine translation/MT) technologies, such as Google Translate and DeepL, as well as applications based on generative artificial intelligence (AI), has brought significant changes to translation practice. While TM facilitates access to cross-language texts, it also presents new challenges in teaching translation. This article aims to examine the challenges and strategies for teaching translation in the era of automation through a literature review. The method used is a literature review of journal articles, books, and conference proceedings published between 2015 and 2025 that are relevant to translation pedagogy, student perceptions, and the ethical implications of using TM. The review identifies seven key challenges: declining student motivation to learn manual translation, the changing role of lecturers to evaluators and post-editors, difficulties in assessing the authenticity of student work, issues of ethics and academic integrity, the need for curriculum adaptation, differences in the quality of TM across languages, and resistance to and over-reliance on TM. Strategies proposed include integrating post-editing into the curriculum, process-based assessment, ethical education in the use of TM, critical learning by comparing human and machine translation results, and translation training for low-resource languages. This article concludes that lecturers play a role not only as teachers of translation techniques but also as facilitators of digital literacy and academic ethics. Recommendations are provided for further empirical research to validate this strategy in the local context of higher education.
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