Abstract
This article explores the dynamic landscape of Computer-Assisted Translation (CAT) tools, aiming to identify the key skills employed by language professionals and the essential competencies students should acquire. Employing a survey methodology, responses from a diverse pool of translators shed light on CAT tool preferences, termbase utilization, and automation practices. RWS Trados and memoQ emerge as frontrunners, underlining the versatility required in contemporary translation workflows. While over 80% adopt quality assurance features and ensure precise tag transfers, challenges persist in reusing termbases across projects.ile conversion skills, particularly.tmx conversions, prove significant for translation memory management. Automation practices such as term extraction and pretranslation reveal varied adoption, signaling opportunities for tailored education. Despite limited current usage, machine translation plug-ins and AI present strategic advantages for translators in training, with the emergence of technologies like ChatGPT anticipated to drive a surge in usage Encouragingly, the article concludes that the emergence of AI does not diminish CAT tool usage, positioning them as integral to the evolving language services landscape.
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