@inproceedings{dogru-2026-evaluative,
title = "Evaluative Judgement in Teaching {AI}-based Translation: A Class-room Case Study of {AI}-Mediated Translation and Post-Editing",
author = "Dogru, Gokhan",
editor = {Kr{\"u}ger, Ralph and
Kenny, Dorothy and
Castilho, Sheila and
{\'A}lvarez-Vidal, Sergi and
Aranberri, Nora and
Ginel, Mar{\'i}a Isabel Rivas and
Hackenbuchner, Jani{\c{c}}a},
booktitle = "Proceedings of the 1st International Workshop on Teaching {AI}-Based Translation and Technologies ({TAITT} 2026)",
month = jun,
year = "2026",
address = "Tilburg, the Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.taitt-1.5/",
pages = "36--48",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Evaluative Judgement in Teaching AI-based Translation: A Class-room Case Study of AI-Mediated Translation and Post-Editing
%A Dogru, Gokhan
%Y Krüger, Ralph
%Y Kenny, Dorothy
%Y Castilho, Sheila
%Y Álvarez-Vidal, Sergi
%Y Aranberri, Nora
%Y Ginel, María Isabel Rivas
%Y Hackenbuchner, Janiça
%S Proceedings of the 1st International Workshop on Teaching AI-Based Translation and Technologies (TAITT 2026)
%D 2026
%8 June
%I European Association for Machine Translation
%C Tilburg, the Netherlands
%F dogru-2026-evaluative
%X 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.
%U https://aclanthology.org/2026.taitt-1.5/
%P 36-48
Markdown (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)
ACL