Operationalising human–AI interaction requirements through DigComp 3.0 for translator education

Generative Artificial Intelligence (GenAI) is changing the landscape of translation, expanding translators’ involvement in interaction configurations, evaluation of AI-generated outputs, and governance of risks within augmented translation environments. This study defines Human–AI Interaction in Translation (HAIT) as GenAI-mediated component of the human-centred augmented translation process, where GenAI is integrated into translation technologies and conversational large language models. Translation education lacks a developmental structure that connects these emerging requirements and their related digital competences to curriculum design. This study proposes a workflow-derived operationalisation of HAIT for translator education. A conceptual functional analysis of augmented translation workflows produced eight HAIT requirements, which were mapped to the 21 competences of the European Digital Competence Framework (DigComp 3.0) by assessing 168 potential relationships for functional correspondence, scope fidelity, and traceable justification. The analysis accepted 37 relationships among 16 competences and identified translation-specific residues that require curricular supplementation. The operationalised requirements were organised into a proposed four-level progression informed by DigComp proficiency levels. An exploratory mixed-methods pilot with 42 second-year translation students examined perceived relevance after contextualised exposure in a course-embedded intervention. Students broadly perceived DigComp as relevant to translation education and practice, particularly focusing on competences related to familiar topics such as privacy protection, device security, copyright, information retrieval, communication and collaboration, with limited attention to information evaluation and programming competences. Their responses were less explicit about AI-generated output evaluation, prompting and in-context steering, grounding configuration, and risk-responsive actions, highlighting the value of a spiral curriculum for competence development and Quality Education (SDG4).

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Journal
Discover Artificial Intelligence
Published
2026-10-29
DOI
https://doi.org/10.1007/s44163-026-02581-6
Primary Topic
Translation Studies and Practices
Type
article
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article

Operationalising human–AI interaction requirements through DigComp 3.0 for translator education

Abrar Fawwaz AlAbed-AlHaq, Nader Al-Refai, Somia Qudah
3 links
Discover Artificial Intelligence
Translation Studies and Practices
article

Operationalising human–AI interaction requirements through DigComp 3.0 for translator education

Abrar Fawwaz AlAbed-AlHaq, Nader Al-Refai, Somia Qudah
article en

Abstract

Generative Artificial Intelligence (GenAI) is changing the landscape of translation, expanding translators’ involvement in interaction configurations, evaluation of AI-generated outputs, and governance of risks within augmented translation environments. This study defines Human–AI Interaction in Translation (HAIT) as GenAI-mediated component of the human-centred augmented translation process, where GenAI is integrated into translation technologies and conversational large language models. Translation education lacks a developmental structure that connects these emerging requirements and their related digital competences to curriculum design. This study proposes a workflow-derived operationalisation of HAIT for translator education. A conceptual functional analysis of augmented translation workflows produced eight HAIT requirements, which were mapped to the 21 competences of the European Digital Competence Framework (DigComp 3.0) by assessing 168 potential relationships for functional correspondence, scope fidelity, and traceable justification. The analysis accepted 37 relationships among 16 competences and identified translation-specific residues that require curricular supplementation. The operationalised requirements were organised into a proposed four-level progression informed by DigComp proficiency levels. An exploratory mixed-methods pilot with 42 second-year translation students examined perceived relevance after contextualised exposure in a course-embedded intervention. Students broadly perceived DigComp as relevant to translation education and practice, particularly focusing on competences related to familiar topics such as privacy protection, device security, copyright, information retrieval, communication and collaboration, with limited attention to information evaluation and programming competences. Their responses were less explicit about AI-generated output evaluation, prompting and in-context steering, grounding configuration, and risk-responsive actions, highlighting the value of a spiral curriculum for competence development and Quality Education (SDG4).

Discover Artificial IntelligenceVol. 6(1)
Yarmouk University (JO)
Quality education
Openalex Percentile: Top 3%
Translation Studies and Practices
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