Journal of Translation and Language Studies

Computer-Assisted Consecutive Interpreting and Its Impact on Student Interpreters: An Empirical Study Using iFLYTEK Hearing

Qanyi Wang — 2025-12-28

With the rapid advancement of artificial intelligence technology, the application of Computer-Assisted Interpretation (CAI) in the field of Consecutive Interpretation (CI) has become increasingly prevalent. Existing research predominantly focuses on the synergistic effects of AI tools and professional interpreters in Simultaneous Interpretation (SI) scenarios, while the efficacy of CAI for student interpreters in CI tasks remains underexplored. This study employs empirical analysis to address two core questions: (1) What impact does CAI have on the interpreting quality of student interpreters in CI? (2) What underlying factors mediate this impact?

Adopting a controlled experimental design, the study recruited 11 third- and fourth-year English majors (all with less than 12 months of interpreting training) as participants. Each participant performed two interpreting tasks of comparable theme, length, and difficulty, with the variable being "use or non-use of CAI tools." Quantitative and qualitative analyses were conducted using iFlytek Hearing for real-time transcription and ERNIE Bot for quality evaluation assistance. Questionnaires and semi-structured interviews were utilized to measure and collect participants’ experiential feedback. Data were analyzed using SPSS software, yielding the following conclusions: Findings suggest that while CAI did not significantly improve overall interpreting quality, it partially supported memory and accuracy. However, cognitive overload and interpreter anxiety limited its effectiveness. These results provide empirical evidence for refining CAI integration in interpreter training. However, this experiment, with no training exposure and a rather small sample size, is more of an exploratory study.

The Use of Machine Translation in Children’s Literature: A Case Study on Robinson Crusoe Based on Children’s Opinions

ALPER BALADIN — 2025-12-28

Research on machine translation (MT) started before the discipline of Translation Studies was even named. Although MT lost its popularity as a research topic from time to time, it has been widely researched in the related literature over the last 20 years. On the other hand, children’s literature, as a discrete topic, has a similar research history to MT. This study aims to question the current use of MT in children’s literature and to explore children’s views on recent MT outputs of children’s literature. The present qualitative research used a case study methodology. Two Turkish MT outputs of Robinson Crusoe, published by Oxford University Press for children in 2000, were collected through DeepL and Google Translate. These two MT outputs were read by the participants who were four children aged 10-12. These participants were chosen by adapting the convenience sampling method. Their opinions about the translations of children’s literature were collected through in-depth interviews. The results of the study mainly reveal that the participants preferred the MT output of DeepL for a number of reasons, although they stated that they understood both MT outputs. In the current study, children’s preferences were shown to vary in response to fluency, the use of regular sentences, correct grammar, and punctuation in those MT outputs. In addition, it was uncovered that the older the participants got, the less their need for visuals existed while comprehending the texts. As a result, it was observed that the MT output of DeepL produced promising translation solutions in the genre of children's literature in the Turkish-English language pair.

Income as a Moderator between Classroom Management Style and Emo-Educational Divorce: A Mixed-Methods Study of Iranian EFL Teachers

Niloofar Aghajani — 2025-12-28

Emo-educational divorce, a newly-coined concept in language teaching, refers to the loss of emotional involvement in education or a particular course. This study, adopting a sequential mixed-methods design, determines whether teacher income has any moderating effect on the relationship between classroom management style and emo-educational divorce. Moreover, the study aims to explore the teachers’ opinions regarding the role of income in the relationship between classroom management style and emo-educational divorce. In doing so, 160 English teachers from secondary schools participated in the study based on criterion sampling, and a pool of six teachers participated in the qualitative phase of the study based on purposive sampling. A number of instruments were employed to measure classroom management style and emo-educational divorce. To analyze the data, the Pearson product-moment correlation, One-way MANOVA, theme-based categorization including inter-coder reliability were conducted. The results confirmed a medium, negative correlation between classroom management style and emo-educational divorce. Moreover, the results showed that income could moderate the relationship between classroom management style and emo-educational divorce. Finally, regarding EFL teachers’ reactions to the role of income in the linkage between classroom management style and emo-educational divorce, the results of semi-structured interviews revealed nine common themes, including lack of buoyancy, demotivation, structured classroom, burnout, money, active participation, engagement, the dynamic nature of emo-educational divorce, and time-related issues. Finally, practical implications are suggested for EFL learners and teachers.

AI-Driven and Large Language Models-Based Translation of Arabic News Texts into English: A Comparative Evaluation

Abdalwahid Noman — 2025-12-30

The proliferation of artificial intelligence (AI) has profoundly reshaped machine translation, particularly through the advent of Large Language Models (LLMs). This study provides a systematic comparative evaluation of three prominent AI-driven translation tools (Google Translate, Reverso, Yandex) and three state-of-the-art LLMs (ChatGPT-4, Gemini-1.5-Pro, Bing) for translating Arabic news texts into English. Employing a quantitative research design, a corpus of twenty diverse Arabic news articles from major outlets was compiled. Expert-validated human translations served as benchmarks. Translation outputs were analyzed using a three-tiered framework: (1) classification of errors into lexico-semantic, syntactic, and formatting types; (2) performance assessment via a five-point scoring rubric; and (3) determination of accuracy levels. Results reveal that lexico-semantic errors were the most prevalent (45.22%), followed by formatting (32.27%) and syntactic errors (22.50%). Among all systems, ChatGPT-4 demonstrated superior performance, committing the fewest total errors (19 out of 471) and achieving the highest mean accuracy score (7.68/8.00), with 75% of its outputs rated as "highly accurate." In stark contrast, the AI-driven tool Reverso performed least effectively, recording the highest error count (128) and the lowest mean score (5.94/8.00). The findings establish a clear performance hierarchy, indicating that LLMs, especially ChatGPT-4, significantly outperform traditional AI-driven tools in handling the linguistic and contextual complexities of Arabic news translation. However, persistent error patterns underscore the continued necessity for human post-editing to ensure precision in professional and media-specific translation contexts.