Boosting Academic Buoyancy and Resilience in Iranian EFL Learners through an AI Translation Bot: A Mixed Method Study
محورهای موضوعی : آموزش زبان با کمک فن اوری
1 - Department of English, ShQ.C., Islamic Azad University, Shahr-e Qods, Iran
کلید واژه: buoyancy index, academic resilience, artificial intelligence bots, translation apps,
چکیده مقاله :
Growing interest in translation-oriented Artificial Intelligence reflects its potential to enhance translation quality, yet its effects on learners’ academic buoyancy and resilience remain underexplored. The present research aimed to examine the effects of using the Camera Translator Application on improving academic buoyancy and resilience indices. This study employed a mixed-methods design, consisting of a quasi-experimental pretest–posttest design in the quantitative phase and post-treatment interviews in the qualitative phase. From a pool of 118 junior English translation undergraduates at Islamic Azad University, Tehran, 44 intermediate EFL participants were selected using the Oxford Placement Test (OPT) and were randomly assigned to experimental and control groups. The treatment phase lasted 13 weeks, with one 90-minute session per week, during the second semester of the 2024–2025 academic year. Participants in the experimental group were instructed to use the AI-based Camera Translator Application, whereas those in the control group followed the conventional approach to translation courses. Participants in both groups completed the Academic Buoyancy Scale and the Academic Resilience Scale before and after the treatment, serving as pretests and posttests. The data collected from the pre- and posttests were analyzed using SPSS version 27. The qualitative phase was conducted through interview sessions using semi-structured, open-ended questions. Results from both the quantitative and qualitative phases confirmed that the experimental group significantly outperformed the control group in translation performance, while also showing improvement in academic buoyancy and resilience indices. The findings may benefit stakeholders, practitioners, materials developers, and syllabus designers by encouraging the adaptation and integration of new technologies, such as AI bots, into the curriculum to enrich translation performance and enhance practitioners’ buoyancy and resilience.
Growing interest in translation-oriented Artificial Intelligence reflects its potential to enhance translation quality, yet its effects on learners’ academic buoyancy and resilience remain underexplored. The present research aimed to examine the effects of using the Camera Translator Application on improving academic buoyancy and resilience indices. This study employed a mixed-methods design, consisting of a quasi-experimental pretest–posttest design in the quantitative phase and post-treatment interviews in the qualitative phase. From a pool of 118 junior English translation undergraduates at Islamic Azad University, Tehran, 44 intermediate EFL participants were selected using the Oxford Placement Test (OPT) and were randomly assigned to experimental and control groups. The treatment phase lasted 13 weeks, with one 90-minute session per week, during the second semester of the 2024–2025 academic year. Participants in the experimental group were instructed to use the AI-based Camera Translator Application, whereas those in the control group followed the conventional approach to translation courses. Participants in both groups completed the Academic Buoyancy Scale and the Academic Resilience Scale before and after the treatment, serving as pretests and posttests. The data collected from the pre- and posttests were analyzed using SPSS version 27. The qualitative phase was conducted through interview sessions using semi-structured, open-ended questions. Results from both the quantitative and qualitative phases confirmed that the experimental group significantly outperformed the control group in translation performance, while also showing improvement in academic buoyancy and resilience indices. The findings may benefit stakeholders, practitioners, materials developers, and syllabus designers by encouraging the adaptation and integration of new technologies, such as AI bots, into the curriculum to enrich translation performance and enhance practitioners’ buoyancy and resilience.
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