GTALL: A GNMT Model for the Future of Foreign Language Education (مقاله علمی وزارت علوم)

چکیده

The world of foreign language education has been immensely influenced by the glory of emergent machine translation (MT) technologies including Google Translate (GT) (Knowles, 2022). Considering that end users' perceptions reflect GT practicality, ample research has been conducted regarding language learners’ perceptions on GT use. Yet, investigating Iranian student teachers' perceptions on the use of GT as an ICALL tool for language learning in higher education has been underestimated. To bridge this gap, semi-structured interviews with twelve student teachers, who were selected through purposive convenience sampling, were conducted employing qualitative constructivist grounded theory methodology. Data were analyzed based on the grounded theory data coding principles (open, axial, and selective) using the MAXQDA 2020 software. A model of GT use in language learning, entitled ‘Google Translate-Assisted Language Learning (GTALL) was proposed. The three main categories (i.e. GT familiarity and use, Perceptions, and legitimacy) along with 35 sub-categories at two levels supported our core category ‘implementation of GT in language learning’. The results demonstrated considerable pedagogical implications for educational stakeholders. For administrators, to appreciate contemporary pedagogical transformations to fulfill new generation’s needs. For professors, to improve digital literacy, welcome emergent technologies, and bring them into their learners’ service for greater educational achievements, and for language learners, to develop technological skills that guarantee wise and efficient human-machine interactions.