Evaluative Judgement in Teaching AI-based Translation: A Class-room Case Study of AI-Mediated Translation and Post-Editing
arXiv:2606.15483v1 Announce Type: new Abstract: Drawing on 23 anonymized student pro-jects from a fourth-year Machine Transla-tion and Post-editing course in a BA-level translation programme, this paper exam-ines how structured comparison of gen-eral-purpose LLMs and online MT sys-tems can elicit evaluative judgement in AI-mediated translation. Students translat-ed short specialised English Wikipedia texts into Catalan or Spanish, generated four system outputs, evaluated them using automatic metrics and human adequa-cy/fluency assessment, selected one output for post-editing, and justified the
The proliferation of sophisticated AI-based translation tools necessitates new pedagogical approaches to teach translation students how to effectively use and evaluate these systems.
Understanding how to integrate AI into translation education is crucial for preparing future translators to work with advanced tools, ensuring quality and critical evaluation skills remain central.
Translation education curricula will increasingly need to incorporate structured methodologies for AI-mediated translation and post-editing, shifting from traditional human-only translation paradigms.
- · Translation software developers
- · Language service providers adopting AI
- · Universities with adaptive translation programs
- · Students proficient in AI-mediated translation
- · Traditional translation educators
- · Translation programs resistant to AI integration
- · Translators without AI proficiency
The curriculum for translation studies will evolve to include mandatory AI evaluation and post-editing modules.
There will be a greater demand for translation professionals skilled in critically assessing and refining AI-generated content rather than just translating from scratch.
The role of the human translator shifts further toward quality assurance, ethical oversight, and handling nuanced or sensitive content that AI struggles with, potentially altering industry compensation models.
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