<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="menzel-2026-teaching">
    <titleInfo>
        <title>Teaching linguistic prompt control for LLM based translation: A classroom approach to developing critical and responsible AI literacy</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Katrin</namePart>
        <namePart type="family">Menzel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 1st International Workshop on Teaching AI-Based Translation and Technologies (TAITT 2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ralph</namePart>
            <namePart type="family">Krüger</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dorothy</namePart>
            <namePart type="family">Kenny</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sheila</namePart>
            <namePart type="family">Castilho</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sergi</namePart>
            <namePart type="family">Álvarez-Vidal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nora</namePart>
            <namePart type="family">Aranberri</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">María</namePart>
            <namePart type="given">Isabel</namePart>
            <namePart type="given">Rivas</namePart>
            <namePart type="family">Ginel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Janiça</namePart>
            <namePart type="family">Hackenbuchner</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>European Association for Machine Translation</publisher>
            <place>
                <placeTerm type="text">Tilburg, the Netherlands</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>As Large Language Models (LLMs) are increasingly integrated into professional translation, there is a growing need for pedagogical frameworks that move beyond simple trial-and-error prompting across any available tools used for translation tasks. This paper presents a framework developed in a university MA seminar on translation to foster responsible AI literacy as well as linguistically grounded prompt control and structured prompting. The approach also emphasises that AI is understood as a tool for suggesting possible translation solutions, while human translators remain the final decision makers. Integrating translation-oriented text analysis and corpus-informed feature extraction from parallel and compa-rable data, the approach teaches students to develop register-specific and model-interpretable instructions when translating specialised texts with the assistance of generative AI tools. During a one-semester course, students familiarised with prompting strategies and different tools, including commercial, freely available models, GDPR-compliant institutional infrastructure and local models. These steps and the comparative evaluation of these tools allowed the students to identify optimal configurations that conform best to professional quality standards for specialised texts, and they ultimately led to highly improved translation output compared to unsteered model results.</abstract>
    <identifier type="citekey">menzel-2026-teaching</identifier>
    <location>
        <url>https://aclanthology.org/2026.taitt-1.4/</url>
    </location>
    <part>
        <date>2026-06</date>
        <extent unit="page">
            <start>28</start>
            <end>35</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Teaching linguistic prompt control for LLM based translation: A classroom approach to developing critical and responsible AI literacy
%A Menzel, Katrin
%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 menzel-2026-teaching
%X As Large Language Models (LLMs) are increasingly integrated into professional translation, there is a growing need for pedagogical frameworks that move beyond simple trial-and-error prompting across any available tools used for translation tasks. This paper presents a framework developed in a university MA seminar on translation to foster responsible AI literacy as well as linguistically grounded prompt control and structured prompting. The approach also emphasises that AI is understood as a tool for suggesting possible translation solutions, while human translators remain the final decision makers. Integrating translation-oriented text analysis and corpus-informed feature extraction from parallel and compa-rable data, the approach teaches students to develop register-specific and model-interpretable instructions when translating specialised texts with the assistance of generative AI tools. During a one-semester course, students familiarised with prompting strategies and different tools, including commercial, freely available models, GDPR-compliant institutional infrastructure and local models. These steps and the comparative evaluation of these tools allowed the students to identify optimal configurations that conform best to professional quality standards for specialised texts, and they ultimately led to highly improved translation output compared to unsteered model results.
%U https://aclanthology.org/2026.taitt-1.4/
%P 28-35

Markdown (Informal)

[Teaching linguistic prompt control for LLM based translation: A classroom approach to developing critical and responsible AI literacy](https://aclanthology.org/2026.taitt-1.4/) (Menzel, TAITT 2026)

• Teaching linguistic prompt control for LLM based translation: A classroom approach to developing critical and responsible AI literacy (Menzel, TAITT 2026)

ACL

• Katrin Menzel. 2026. Teaching linguistic prompt control for LLM based translation: A classroom approach to developing critical and responsible AI literacy. In Proceedings of the 1st International Workshop on Teaching AI-Based Translation and Technologies (TAITT 2026), pages 28–35, Tilburg, the Netherlands. European Association for Machine Translation.