Earticle

Recent advances in artificial intelligence technology have fundamentally transformed learning paradigms. This shift has changed the educational focus from assessing “what and how much learners know” to understanding “how they interpret, internalize, and apply knowledge.” This transition is particularly important in theory-oriented courses that emphasize conceptual frameworks and theoretical foundations, necessitating careful consideration and reflection on evolving learning environments. This study designed and implemented a theory-based translation and interpreting course that utilized educational technology tools to create a blended “online + offline” learning environment. The goal was to promote interaction among learners and between learners and the instructor while facilitating collaborative knowledge construction. The present study investigates learner experiences and responses regarding peer interaction, collaborative learning processes, and the pedagogical effectiveness of integrating educational technology. The objective is to derive educational implications for future translation and interpreting pedagogy.

With the global spread of K-culture, original Korean musicals (K-musicals) are increasingly exported and supertitled for foreign audiences in Korea. As Chinese nationals form the largest group of foreign residents, and exports to China grow, the translation of Chinese supertitles is gaining importance. Yet research on this topic remains limited. This study examines the characteristics of Chinese lyrics and the challenges of translating Korean lyrics directly into Chinese. Focusing on the musical After Life, it proposes strategies that accommodate the unique demands of Chinese lyrics, which prioritize implicit and concise expression (xiěyìxìng), parallel structure (duìjù), rhyme, and repetition—elements often lost in direct translation. As specific strategeis, the translator proposes using sentence-final nouns, idioms, four-character expressions (chéngyǔ), and substitution and omission to achieve syllable alignment and structural cohesion. These techniques enhance rhythm and rhyme through restructured imagery and nominalization, while reinforcing poetic effects through repetition. Collectively, these strategies clarify and condense meaning while enriching lyrical subtlety. Though limited to one production and not easily generalizable to oral translation, this study offers concrete, experience-based subtitling strategies.

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.

This study aims to design instructional strategies for fostering multimodal literacy in translation education and to discuss their educational effectiveness. To achieve the goal, it shares instructional experiences from an English-Korean translation graduate course, and analyzes learners' satisfaction and the effectiveness of the program. The course is structured to help learners understand and accept that communication in translation involves not only linguistic elements but also various semiotic modes. The course for improving multimodal literacy is organized into a five-stage process: cultivating attitudes, building knowledge, analyzing multimodal texts, experiencing multimodal text production, and providing feedback. Through these stages, learners engage in a process that spans fromchanging their attitudes toward multimodal texts to actually producing such texts, thereby developing multimodal literacy. The student satisfaction survey revealed that this course positively impacted learners' awareness and competencies, noting that the instructional approach is effective in addressing the changing roles of translators in a digital environment, where the need for diverse expertise is increasingly emphasized. This suggests that such educational strategies can effectively respond to contemporary demands in the field of translation.

This study provides a meta-level overview of Korean detective fiction translation research by addressing three research questions: (1) What are the central keywords in this field? (2) How have the thematic and chronological trends changed over time? (3) What are the cluster structures and interconnections revealed through keyword network analysis? To answer these questions, we analyzed articles and dissertations published in key Korean academic journals, using the big data text-mining tool TEXTOMand the network analysis software UCINET 6. The analysis identified four main clusters: (1) genre reception and translation strategies, (2) literary experimentation and genre transformation, (3) genre structure and cultural reception, and (4) colonial-era translations and the transplantation of detective narratives. These findings highlight how detective fiction translation research has evolved from isolated case studies to a more interconnected and structured academic field. This quantitative approach complements previous qualitative studies and provides a broader visualization of the field’s structure and thematic development. However, the study acknowledges limitations in data scope and the exclusive reliance on quantitative methods. Future research should integrate qualitative analyses and explore the reception of translations by readers, as well as compare detective fiction translation practices with those of other genre literatures.

The study presents a quantitative comparative analysis of average reader ratings for 38 Korean literary works translated into English. Data were collected from Kyobo Moonko, Korea’s largest bookstore, and Goodreads, a prominent American book platform. The analysis reveals three key findings. Firstly, there is no correlation between the number of reader ratings and the average rating on Goodreads, indicating that a title’s popularity does not reliably predict its reception among English-speaking readers. Secondly, no correlation was found between ratings on Kyobo and those on Goodreads, suggesting that works highly rated by Korean readers may not be equally well received by English readers. Lastly, thriller and mystery novels received relatively lower average ratings on Goodreads compared to general fiction and fantasy titles, highlighting a potential need for further development in these genres to appeal to English-speaking audiences.

Fluency management is a persistent challenge in interpreter training, particularly when students work into a B language. This study investigates how syntactic complexity in the target text (TT) affects disfluency in Korean-to-English consecutive interpreting by student interpreters. While previous research has examined cognitive load and general fluency, few studies have explored TT sentence complexity as a predictor of disfluency patterns. Drawing on data fromsix student interpreters, this study analyses disfluency frequency and type across sentences classified by Mean Dependency Distance (MDD). The findings show that TT syntactic complexity is positively associated with overall disfluency, with silent pauses most consistently affected, followed by fillers and self-repairs, while repetitions showed no reliable pattern. This relationship was confirmed through both linear and non-parametric analyses, revealing how specific disfluency types reflect structural processing strain. By focusing on a typologically distant language pair and analysing disfluency at the sentence level, the study adds empirical depth to fluency research in interpreting. It also offers pedagogical implications for helping trainees manage TT complexity more strategically during B language production.