Machine Translation-Based Learning as an Alternative Learning Strategy in the Translation Course for Fifth-Semester Students at Universitas Indraprasta PGRI - Astri Indriana Octavita; Tiarma Ika Yuliana; Rima Novia Ulfa (Pusat Penerjemah dan Penyunting Universitas Indraprasta PGRI, 2026)
The rapid development of artificial intelligence (AI) has brought major changes to education, including translation learning. One of its most visible implementations is machine translation (MT) — tools such as Google Translate and DeepL — which are now able to produce contextual translations through Neural Machine Translation (NMT) systems. This study aims to describe the implementation of MT-based learning in the Translation course and to analyze students' perceptions of its effectiveness. A descriptive qualitative approach was employed, involving fifth-semester students and the lecturer of the Translation course at Universitas Indraprasta PGRI, Jakarta, as participants. Data were collected through observation, in-depth interviews, and documentation, and were analyzed using the interactive model of Miles, Huberman, and Saldaña. The finding shows that integrating machine translation into the learning process improved students' translation quality, critical thinking, linguistic awareness, and learning autonomy. Students used MT not as a substitute for their own translation ability but as an initial aid that was followed by analysis, discussion, and post-editing. The study concludes that MT-based learning can serve as an innovative and pedagogically sound alternative strategy for teaching Translation in higher education, provided it is guided by lecturers within a critical and ethical framework.