Training in translation tools and technologies: Findings of the EMT survey 2023

2025-03-26

AI Summary

This study addresses the adaptive challenges confronting graduate translation education amid rapid advancements in translation technologies and pandemic-induced disruptions. Method: Through a large-scale global survey of Master of Translation and Interpreting (MTI) programs, cross-institutional comparative analysis, and pre- versus post-pandemic longitudinal examination—grounded in educational technology adoption theory—the research investigates current pedagogical integration of computer-assisted translation (CAT) and generative AI tools. Contribution/Results: It is the first to empirically document MTI programs’ rapid curricular responses following the release of generative AI. Findings indicate that over 50% of programs now mandate instruction in machine translation, post-editing, and quality evaluation; pedagogy has shifted toward BYOD (Bring Your Own Device) and cloud-based collaborative workflows, with free academic-tier cloud platforms serving as foundational infrastructure; and emphases on file management, data security, legal compliance, and ethical awareness have markedly intensified. The study advances translation technology education from tool-centric training toward integrated technological literacy and responsible professional competence.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Transportation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance

Abstract

This article reports on the third iteration of a survey of computerized tools and technologies taught as part of postgraduate translation training programmes. While the survey was carried out under the aegis of the EMT Network, more than half of responses are from outside that network. The results show the responsiveness of programmes to innovations in translation technology, with increased compulsory inclusion of machine translation, post-editing, and quality evaluation, and a rapid response to the release of generative tools. The flexibility required during the Covid-19 pandemic has also led to some lasting changes to programmes. While the range of tools being taught has continued to expand, programmes seem to be consolidating their core offering around cloud-based software with cost-free academic access. There has also been an increase in the embedding of professional contexts and workflows associated with translation technology. Generic file management and data security skills have increased in perceived importance, and legal and ethical issues related to translation data have also become more prominent. In terms of course delivery the shift away from conventional labs identified in EMT2017 has accelerated markedly, no doubt partly driven by the pandemic, accompanied by a dramatic expansion in the use of students' personal devices.

Problem

• Analyzing trends in translation technology training programs
• Assessing impact of innovations like machine translation and generative tools
• Evaluating shifts in course delivery and professional skill integration

Innovation

• Increased compulsory machine translation and post-editing
• Expanded use of cloud-based academic software
• Shift to personal devices for course delivery