Speaker
Description
In the context of Vietnam’s ongoing transition from English as a Foreign Language (EFL) to English as a Second Language (ESL), there is an increasing demand for developing learners’ real-time communicative competence, particularly in Interpreting. This work-in-progress study investigates the effectiveness of the TARI chatbot as an AI-assisted tool in enhancing English–Vietnamese interpreting skills among undergraduate students majoring in English. This study employed course-aligned, topic-based interpreting tasks. The topics were derived from the official course syllabus, ensuring alignment with intended learning outcomes and real-world communication contexts. During the intervention, students engaged in structured interpreting practice using input provided by the TARI chatbot, followed by AI-generated feedback and repeated performance to reinforce skill development. Adopting a quantitative research design, data were collected from three intact classes (N ≈ 90–120) enrolled in an interpreting-related course. A Likert-scale questionnaire was administered to measure students’ self-reported improvements in interpreting accuracy, fluency, listening comprehension, and confidence, as well as their perceptions of the chatbot’s usability and pedagogical value. The data were analyzed using SPSS 26, including descriptive statistics and inferential analyses. The findings indicate that the use of the TARI chatbot supports the development of interpreting-related sub-skills. Students also reported positive perceptions of AI-assisted practice, emphasizing its role in creating an English-rich, low-anxiety learning environment aligned with ESL-oriented pedagogy. The study contributes to the growing body of research on AI integration in English language teaching and offers practical implications for incorporating chatbot-supported, curriculum-aligned interpreting activities in similar EFL-to-ESL transition contexts.
