Development and validation of machine translation literacy scale for translation education - Junho Lee; Sowon Ahn; Yeong-Houn Yi (Australian Journal of Applied Linguistics)
The rise of machine translation demands a fundamental shift in both translators’ roles and educational approaches. However, translation education research and practice have struggled to keep pace with the latest developments. To bridge the pedagogical gap, this study conceptualises machine translation literacy within the context of translation education and develops a diagnostic scale to assess students’ proficiency in machine translation literacy. Drawing on a review of existing literature and case studies on machine translation literacy applications in translation pedagogy and related disciplines, this study establishes an initial framework for machine translation literacy. To validate the structure and reliability of a proposed model for machine translation (MT) literacy, data were collected from 389 undergraduate and graduate translation students in South Korea and analysed using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). The analysis yielded a robust six-factor model encompassing risk management, verification and correction, basic knowledge, operational knowledge, flexibility, and transparency. This study provides a reliable assessment model for evaluating students’ machine translation literacy and lays the groundwork for the translation education planning at both macro and micro levels. Additionally, it positions machine translation literacy as an essential component within the broader theoretical framework of translation education research and serves as a foundation for expanding research into interdisciplinary domains.