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Evaluative Judgement in Teaching AI-based Translation: A Class-room Case Study of AI-Mediated Translation and Post-Editing

A study of 23 student projects in a fourth-year Machine Translation and Post-editing course found that students did not treat automatic metrics as final authority when selecting machine translation output for post-editing, according to a paper by Gokhan Dogru published in the Proceedings of the 1st International Workshop on Teaching AI-Based Translation and Technologies (TAITT 2026). Students translated short specialised English Wikipedia texts into Catalan or Spanish, generated four system outputs, evaluated them with automatic metrics and human adequacy/fluency assessment, then justified their post-editing choice in written reports; final selections often diverged from metric rankings and were justified through adequacy, fluency, terminology, and expected post-editing effort. The analysis combined descriptive counts from all 23 projects with qualitative coding of the 22 cases supported by written reports, and the paper appears in the TAITT 2026 proceedings, pages 36–48, published by the European Association for Machine Translation in Tilburg, the Netherlands, in June 2026.

read2 min views1 publishedSep 17, 2026
Evaluative Judgement in Teaching AI-based Translation: A Class-room Case Study of AI-Mediated Translation and Post-Editing
Image: Aclanthology (auto-discovered)
Abstract

Drawing on 23 student projects from a fourth-year Machine Translation and Post-editing course, this paper examines how asking students to compare LLM and NMT outputs, interpret metric results, and justify a post-editing choice reveals their evaluative judgement. Students translated short specialised English Wikipedia texts into Catalan or Spanish, generated four system outputs, evaluated them using automatic metrics and human adequacy/fluency assessment, selected one output for post-editing, and justified their decision in written reports. The analysis combines descriptive counts from 23 projects with qualitative coding of the 22 cases sup-ported by written reports. Results show that students did not treat automatic metrics as final authority: final post-editing selections often diverged from metric rankings and were justified through adequacy, fluency, terminology, and expected post-editing effort. The study therefore does not compare systems under benchmark conditions; it analyses how students justified system choice within an au-thentic classroom assignment.

- Anthology ID:
- 2026.taitt-1.5
- Volume:
- [Proceedings of the 1st International Workshop on Teaching AI-Based Translation and Technologies (TAITT 2026)](https://aclanthology.org/volumes/2026.taitt-1/)
- Month:
- Venues:
- [TAITT](https://aclanthology.org/venues/taitt/) |[WS](https://aclanthology.org/venues/ws/)
- SIG:
- Publisher:
  • European Association for Machine Translation
- Note:
- Pages:
  • 36–48
- Language:
- URL:
- [https://aclanthology.org/2026.taitt-1.5/](https://aclanthology.org/2026.taitt-1.5/)
- DOI:
- Cite (ACL):
- Cite (Informal):
- [Evaluative Judgement in Teaching AI-based Translation: A Class-room Case Study of AI-Mediated Translation and Post-Editing](https://aclanthology.org/2026.taitt-1.5/) (Dogru, TAITT 2026)
- PDF:
- [https://aclanthology.org/2026.taitt-1.5.pdf](https://aclanthology.org/2026.taitt-1.5.pdf)
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