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A Subjectivity-Oriented Pedagogical Model for Human-Machine Collaborative Translation
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Abstract
Large language models are reshaping translation practice in the age of AI, bringing profound challenges to translation education. Many studies have focused on the construction of technology-empowered teaching models or competence-oriented teaching models. Translator subjectivity construction remains underexplored. Based on the social-constructivist perspective, the present study investigates the construction of translation subjectivity through teaching prac-tice. From skill training to subjectivity construction in human-AI collaboration translation, this study proposes a three-stage pedagogical model, including subject establishment, subject collaboration, and subject regulation. Across the three stages, a dynamic role evolution of teachers, students, and AI tools has been illustrated with examples from authentic in-class practices in a graduate course. With teachers’ scaffolding support and later gradual withdrawal of scaffolding, students finally develop autonomous decision-making abilities and other higher-order competences required in AI-assisted translation, achieving a human-in-command rather than human-in-the-loop position. The evolving roles demonstrate the progressive pathway of translator subjectivity construction, providing an alternative framework for translation education in the AI era.
Keywords
Translator subjectivity; pedagogical model; human-machine collaborative translation
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How to cite this paper
A Subjectivity-Oriented Pedagogical Model for Human-Machine Collaborative Translation
How to cite this paper: Xiao Zhang. (2026). A Subjectivity-Oriented Pedagogical Model for Human-Machine Collaborative Translation. The Educational Review, USA, 10(7), 398-405.
DOI: http://dx.doi.org/10.26855/er.2026.07.003
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