Aligning Translation Curricula with Technological Advancements; Insights from Artificial Intelligence Researchers and Language Educators

Zuhair Dawood Mohammad Zaghlool and Mohamad Ahmad Saleem Khasawneh

Studies in Media and Communication, 2024, vol. 12, issue 1, 58-70

Abstract: This study expounded on methods, approaches, and strategies for aligning translation curricula with technological enhancements in the effort to train translators who are good to function in the AI-driven translation industry. The study sample consisted of 83 university lecturers in translation studies and 151 artificial intelligence experts from ten Jordanian universities. The study was carried out during the academic year 2022-2023. Moreover, the study design is a survey approach, and the strategy of the research is quantitative. Data was collected using a questionnaire and analysis was conducted using relevant descriptive statistics tools, including the percentile values of the Likert scale, the mean, and the standard deviation. In addition, the findings generally indicate that task-based approach, team-based strategy, blended learning, and reflective system are the main pedagogical strategies and teaching techniques that work optimally for incorporating AI-based translation technologies into translation curricula. Similarly, the findings generated from the data analysis further suggest that the main strategies to ensure alignment of translation curriculum with technological advancements are careful designing of specific lessons for technology in translation, formation of partnerships between industry players (translation industry leaders and AI experts), and translation curriculum designers, continuous review of the curriculum, and inclusion of creativity and critical thinking for the students. Finally, it is concluded that translation curriculum designers must always review the translation curriculum, partner with translation industry leaders and AI experts, and integrate critical thinking and creativity into the translation curriculum system. This is to ensure that the trained translators can function effectively in the AI-driven translation industry.

Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://redfame.com/journal/index.php/smc/article/download/6378/6367 (application/pdf)
https://redfame.com/journal/index.php/smc/article/view/6378 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rfa:smcjnl:v:12:y:2024:i:1:p:58-70

Access Statistics for this article

More articles in Studies in Media and Communication from Redfame publishing Contact information at EDIRC.
Bibliographic data for series maintained by Redfame publishing ().