ANTHONY YOUNG -

EXPLORING MACHINE TRANSLATION: OUTPUT QUALITY, LEARNER REFLECTION, TEACHER DETECTION : DIGITAL LITERACY IN ELT

Advances made in Machine Translation (MT) technology have seen its accuracy and quality improve significantly in recent years. There is substantial evidence that language students use it with increasing frequency for academic purposes (e.g. Enríquez et al., 2020; Jolly & Luciane, 2022; Valijärvi & Tarsoly, 2019), highlighting the need to explore further its potential benefits and limitations. This presentation explains the findings of an investigation on three critical aspects of MT technology (output quality, learner reflection, and teacher detection) and its impact on language education. First, it compares the output quality of three freely available MT networks (DeepL, Google Translate, and Microsoft Bing) by examining six bilingual professors' evaluations of three translated texts. Next, it considers the capacity of MT to promote metalinguistic awareness by analyzing the output and post-survey feedback of 12 university students who carried out a reflective writing task. Finally, this presentation gauges the capacity of native English instructors to detect machine-translated texts by examining the results and post-task feedback of 13 teachers tasked with distinguishing between output generated by learners or MT. By understanding the strengths and limitations of MT technology, educators are better able to make informed decisions about its integration. As MT continues to evolve, this research contributes to ongoing efforts to optimize its application in second-language education settings.

Anthony Young is an associate professor at Aichi University. He has taught English in Japan for 23 years and holds a doctorate in education from the University of Southern Queensland. His research interests include computer-assisted language learning, task-based language teaching, synchronous computer-mediated communication, and machine translation.