@inproceedings{hao-etal-2026-translator,
title = "Translator competence in the age of agentic {AI} orchestration: A ``backcasting'' perspective",
author = "Hao, Yu and
Wu, Elise and
Leung, Ester",
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-1.11/",
pages = "78--84",
abstract = "As an orchestration infrastructure, agentic AI systems now can plan and decompose the pre-defined goals into a sequence of steps, decide on external function calls, and coordinate one LLM or multiple LLMs with specialised roles. In this context, this position paper adopts a future studies ``backcasting'' ap-proach that starts with a desirable future, en-visioned as one in which AI-integrated trans-lation workflows are transparent, accountable, and aligned with human values; it then works backwards to examine how translator exper-tise should be reconceptualised to sustain meaningful human-in-the-loop participation. In this sense, the study first conceptualises the current translation-service provision as a sys-tem structured around managerial, mediation, and authorising roles. It then analyses how these roles may be changed and augmented within the agentic AI-orchestrated workflows. Building on the analysis, we propose a series of competences that should be cultivated to achieve the envisioned future: 1) evaluation grounded in advanced language competence; 2) situated and context-sensitive judgement informed by cultural and experiential knowledge; and 3) strategic procedural plan-ning in the design and oversight of agentic AI-orchestration workflows. The paper con-cludes with recommendations for future peda-gogical development and empirical research."
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<abstract>As an orchestration infrastructure, agentic AI systems now can plan and decompose the pre-defined goals into a sequence of steps, decide on external function calls, and coordinate one LLM or multiple LLMs with specialised roles. In this context, this position paper adopts a future studies “backcasting” ap-proach that starts with a desirable future, en-visioned as one in which AI-integrated trans-lation workflows are transparent, accountable, and aligned with human values; it then works backwards to examine how translator exper-tise should be reconceptualised to sustain meaningful human-in-the-loop participation. In this sense, the study first conceptualises the current translation-service provision as a sys-tem structured around managerial, mediation, and authorising roles. It then analyses how these roles may be changed and augmented within the agentic AI-orchestrated workflows. Building on the analysis, we propose a series of competences that should be cultivated to achieve the envisioned future: 1) evaluation grounded in advanced language competence; 2) situated and context-sensitive judgement informed by cultural and experiential knowledge; and 3) strategic procedural plan-ning in the design and oversight of agentic AI-orchestration workflows. The paper con-cludes with recommendations for future peda-gogical development and empirical research.</abstract>
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%0 Conference Proceedings
%T Translator competence in the age of agentic AI orchestration: A “backcasting” perspective
%A Hao, Yu
%A Wu, Elise
%A Leung, Ester
%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 hao-etal-2026-translator
%X As an orchestration infrastructure, agentic AI systems now can plan and decompose the pre-defined goals into a sequence of steps, decide on external function calls, and coordinate one LLM or multiple LLMs with specialised roles. In this context, this position paper adopts a future studies “backcasting” ap-proach that starts with a desirable future, en-visioned as one in which AI-integrated trans-lation workflows are transparent, accountable, and aligned with human values; it then works backwards to examine how translator exper-tise should be reconceptualised to sustain meaningful human-in-the-loop participation. In this sense, the study first conceptualises the current translation-service provision as a sys-tem structured around managerial, mediation, and authorising roles. It then analyses how these roles may be changed and augmented within the agentic AI-orchestrated workflows. Building on the analysis, we propose a series of competences that should be cultivated to achieve the envisioned future: 1) evaluation grounded in advanced language competence; 2) situated and context-sensitive judgement informed by cultural and experiential knowledge; and 3) strategic procedural plan-ning in the design and oversight of agentic AI-orchestration workflows. The paper con-cludes with recommendations for future peda-gogical development and empirical research.
%U https://aclanthology.org/2026.taitt-1.11/
%P 78-84
Markdown (Informal)
[Translator competence in the age of agentic AI orchestration: A “backcasting” perspective](https://aclanthology.org/2026.taitt-1.11/) (Hao et al., TAITT 2026)
ACL