کتایون اسکویی

In spite of numerous studies on the effect of AIAT on foreign language learning, few have examined its impact on university students’ academic writing proficiency over an extended period. This study, therefore, investigated the effect of Google Translate (GT) on the writing proficiency of English as a Foreign Language (EFL) university students. It examined fluency, lexical density, accuracy, and syntactic complexity across four versions of students’ writings: the pretest, writing with the aid of GT, the posttest, and a retention test conducted two to four months after the treatment. The findings indicated that syntactic complexity, accuracy, and fluency improved when students used GT for writing; however, these measures showed a significant decline in the posttest and retention test without GT. This decrease was more pronounced in lexical density and less so in syntactic complexity. Despite this decline, all factors assessed in the retention test still showed improvement compared to the pretest, indicating the positive effect of GT on students’ foreign language writing performance.

The advent of AI technologies has been influential on the quality of language learning in EFL contexts. A plethora of studies have explored the employment of AI bots in academic writing. Yet, exploring the strategies employed by Iranian EFL learners while using an AI bot to complete academic IELTS writing task 2 has been underestimated. To address the lacuna, this qualitative study computer-recorded eleven participants’ interactions with AI bot to identify the strategies they employed to complete academic IELTS writing task 2. The researchers intended to clarify the intricacies of the human-AI interactions and infer the potential contributions of AI bots for academic writing in EFL settings. The findings revealed that despite the employment of several strategies by the participants to compose high-quality writing tasks, none of them fully trusted the AI outputs to use them without post-editing. They considered the AI bot as a writing assistant, used some revised segments of AI responses and added their own words respectively. The pedagogical implications for language teaching in general, and IELTS-oriented practices in particular to take advantage of the emerging AI technologies for better educational outcomes are provided. Moreover, avenues for future research on AI-assisted Language Learning (AILL) are suggested.

Thanks to the significant achievements in Artificial Intelligence (AI), Machine Translation (MT), in general, and Google Translate (GT), in particular, have been extensively used in all facets of life, including language learning. However, faced with a plethora of research evidence on GT’s educational contributions, erroneous translations create disparity regarding its use in language learning. To address this lacuna, the present study systematically reviewed 10 databases, namely, Web of Science, Scopus, ERIC, ScienceDirect, Taylor & Francis Online, Wiley Online Library, SAGE Journals, Springer Link, Springer Open, and DOAJ. Additionally, it hand searched the reference lists of 44 studies selected to be included in the synthesis from database search along with references cited in three previous systematic reviews on similar topics to capture a comprehensive view of the literature related to the use of GT in language learning between 2010-2021. It reviewed 50 studies witnessing a rise in the number of studies in this area. Studies reported that although significant improvements in the quality of GT led to pedagogical gains and more tendency to implement it in language learning, instructors still distrust it. Accordingly, this research provides pedagogical implications and suggests avenues for future research on the use of GT in language learning.     

This quantitative study aimed to investigate Iranian EFL student teachers’ perceptions on the use of Machine Translation (MT) for foreign language learning in academic context. To this end, 107 EFL student teachers from a women-only state university in Tehran, Iran, completed a recently developed and validated questionnaire in the field. The findings revealed that most participants were familiar with digital technology including MT and its different types such as Google Translate (GT). Satisfied with MT output, the majority of the participants in the study installed MT apps on their smartphones or used its website on their computers to complete assignments or to translate from Persian to English and vice versa. However, they were neutral about whether their instructors confirmed their MT use, or whether they preferred their teachers know they use MT or not. They were also not sure whether consulting MT was against the regulations. The results showed that authorities in the field of foreign language teaching are required to take a positive stand on this emerging technology; in addition, considering the importance of training for both instructors and learners, they should hold workshops for more responsible and effective MT implementation. 

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.