1887
Volume 4, Issue 1
  • ISSN 2799-6190
  • E-ISSN: 2799-8592

Abstract

In the wake of the Fourth Industrial Revolution, artificial intelligence (AI) is rapidly transforming human lives at an unprecedented rate. As this new era begins and technological advancements continue to accelerate, there appears to be a parallel need for corresponding changes and reforms in the field of translation and interpretation education. Indeed, many interpreters and translators now incorporate automated translation tools in their work, and a significant number of researchers are advocating for the application of AI platforms in translation and interpretation education, proposing innovative teaching methods. Among these innovations, various platforms developed specifically for interpreter training can be categorized into training-based platforms, data storage-based platforms, and interpreter material storage-based platforms. This paper delves into the impact of such platforms on translation and interpretation education, with a particular focus on the neighboring country of China, which extensively utilizes Learning Management System (LMS)-based smart cloud platforms, AI platforms, and voice recognition applications in this educational field. Firstly, the analysis of classroom systems based on LMS, such as the iSmart smart educational cloud platform, the SHIYIBAO smart translation and interpretation education platform, and Oia developed in collaboration with Shanghai International Studies University, reveals their usage patterns. Secondly, experiments with applications capable of voice recognition, such as iFLYTEK, are examined. Th dly, the impact of on- screen subtitles displayed on computer monitors on interpreters is considered. These case studies demonstrate that AI platforms can enhance the quality of translation and interpretation, and also significantly alleviate the fear and burden associated with interpreting practice for students. This positive effect, noted during their interpreting exercises, confirms that platform systems incorporating voice recognition and other AI technologies positively influence interpreter education and the quality of interpretation. Additionally, these findings highlight the pressing need for South Korea to actively adopt such platforms in its translation and interpretation education moving forward.

Available under the CC BY-NC 4.0 license.
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2024-04-30
2026-04-21
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References

  1. Baker, Mona
    (1995) Corpora in translation studies: An overview and some suggestions for future research. Target. International Journal of Translation Studies, 7(2), 223–243.
    [Google Scholar]
  2. Chang, Ai Li
    (2019) Junggugui ingongjineung (AI) tongyeok baljeon hyeonhwang bunseok [Analysis of development of AI-based interpretation in China]. The Journal of Translation Studies, 20(5), 163–195.
    [Google Scholar]
  3. Choi, Eunsil
    (2023) Tongyeok hullyeonyong peullaetpom gaebareul wihan gicho yeongu [Basic research for developing a platform for interpreter trainees]. The Journal of Interpretation and Translation Education, 21(4), 125–149. 10.23903/kaited.2023.21.4.006
    https://doi.org/10.23903/kaited.2023.21.4.006 [Google Scholar]
  4. Choi, Moonsun
    (2016) Kopeoseu giban tongyeokak yeongu donghyanggwa sisajeom [A survey of corpus-based interpreting studies: Current trends and suggestions for future research in Korea]. Interpretation and Translation, 18(3), 121–159.
    [Google Scholar]
  5. Chun, Hyun-ju
    (2020) Ingangwa gigyebeonyeogui gongjon paereodaim mosaek: PBL gibanui AI beonyeok tul hwallyong beonyeoksueop unyeong peuroseseoreul jungsimeuro [Searching for the coexistence paradigm of human translator and machine translation: Focusing on PBL-based translation practicum class with AI machine translation tools]. The Journal of Interpretation and Translation Education, 18(4), 59–96.
    [Google Scholar]
  6. Jee, Yun-ju, Sang-Bin Lee and Sun-Woo Lee
    (2023) Hakbubeonyeokjeongongjaui chaet GPT gwallyeon insikgwa chaet GPT beonyeok mit poseuteuediting silheom yeongu [Perceptions and use of ChatGPT: Insights from undergraduate students majoring in Korean-English translation]. Interpreting and Translation Studies, 27(3), 203–226.
    [Google Scholar]
  7. Jung, You-Sun and Hee-Jeong Han
    (2019) Ingongjinung tongbeonyeokpeurogeuraemeul hwalyonghan panmaejunggugeo gyoyukbangan tamsaek [A study on the learning methods of sales Chinese using AI translation program]. The Journal of Chinese Language, Literature and Translation, 441, 351–375. 10.35822/JCLLT.2019.01.44.351
    https://doi.org/10.35822/JCLLT.2019.01.44.351 [Google Scholar]
  8. Lee, Juriae
    (2023) Gisul bojo tongyeok hullyeon (CAIT) tul yeongu gochal — Korona-19 sigi (2020 nyeon ~ 2023 nyeon) reul jungsimeuro [A review of computer-assisted interpreter training (CAIT) tools: Focusing on trends following COVID-19 (2020–2023)]. Interpreting and Translation Studies, 27(4), 63–94. https://www.earticle.net/Article/A438468
    [Google Scholar]
  9. Li, Xiao-long and Meng-jie Wang
    (2018) Ji yu yu yin shi bie APP de tong sheng chuan yi neng li pei yang jiao xue mo shi jian gou yu yan jiu —— yi ke da xun fei yu ji APP wei li [Construction and research of the teaching model of using automatic speech recognition APP in simultaneous interpreting training course — A case study of voice noteas an auxiliary tool]. Technology Enhanced Foreign Language Education, 11, 12–18.
    [Google Scholar]
  10. Lin, Xiaomu
    (2013) Ji suan ji fu zhu ying yi han kou yi shi zheng yan jiu [An empirical study on computer-assisted English-Chinese interpretation] [Master’s thesis]. Shandong Normal University.
    [Google Scholar]
  11. Liu, Jian and Kaibao Hu
    (2015) Duo mo tai kou yi yu liao ku de jian she yu ying yong yan jiu [Research on the construction and application of multimodal interpreting corpus]. Foreign Languages in China, 51, 77–85.
    [Google Scholar]
  12. Park, Chu-Hyoung, Changwoo Lee and Myung-ju Kang
    (2001) Jadong beonyeokgwa CAT- ui hyeonhwanggwa jeonmang [Past, present and future of computer aided translation technology]. Communications of the Korean Institute of Information Scientists and Engineer, 19(10), 19–26.
    [Google Scholar]
  13. Park, Mijung
    (2023) Saengseonghyeong AI-wa gigyebeonyeok — Chaet GPT beonyeogeul tonghan haniltongyeokgyoyuk gochal [Generative AI and machine translation: A study on Korean-Japanese interpretation education through ChatGPT translation]. Interpretation and Translation, 27(3), 27–56.
    [Google Scholar]
  14. Xu, Ran
    (2018) Ji yu yu liao ku ji shu de kou yi yi qian zhun bei mo shi jian gou [Construction of pre-interpretation preparation model based on corpus technology]. Chinese Translators Journal, 39(3), 53–59.
    [Google Scholar]
  15. Yoon, Byung-chen, Il Hoe, Sung Eun Hong and Kang Suk Byun
    (2014) Hanguksueo kopeoseu guchugeul wihan gichoyeongu [Basic research for the development of a Korean Sign Language corpus]. National Institute of Korean Language.
    [Google Scholar]
  16. Zhu, Zhiqiang
    (2015) Yu yin shu zi shi bie fu zhu han ying jiao chuan tan jiu [On the assistance of number recognition during Chinese-English consecutive interpreting] [Master’s thesis]. Beijing Foreign Studies University. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD201502&filename=1015582068.nh
    [Google Scholar]
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