earticle

영어

In undergraduate interpreter training, students are more likely to encounter bilateral interpreting between two working languages than conference interpreting, either during their studies or future careers. However, dialogue interpreting classes often present challenges due to students' limited linguistic competence and lack of subject knowledge. To address these difficulties, this study explores how students use generative AI to create dialogue scripts for interpreting practice, focusing on prompt literacy, AI-generated text features, and learners' perceptions. Students worked in small groups to generate weekly dialogue scripts on assigned topics using ChatGPT. These scripts were then enacted in class as role-plays, with each student taking a participant's role. Spontaneous interpreting was performed by other students selected by the instructor. Each group submitted full logs of their prompts and AI-generated outputs weekly and completed a survey at the end of the semester. The findings indicate that students' prompting strategies generally aligned with expert techniques and improved over time. The generated scripts proved suitable as source texts for interpreting exercises, requiring only minor revisions. Furthermore, the dialogues displayed a strong sense of realismand authenticity, enhancing learner immersion and engagement. Overall, students responded positively to the use of ChatGPT in script preparation. This study highlights the pedagogical potential of generative AI in undergraduate interpreter education.