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Beyond intention: A cognitive-affective-ethical framework for translation students' in-task acceptance of
1School of Humanities and Foreign Languages, Zhejiang Shuren University, Hangzhou, China.
Acta Psychologica
|June 17, 2026
Summary
Student translators accept AI feedback differently based on text type, their initial AI attitudes, and skill level. Acceptance is a complex process influenced by cognitive, affective, and ethical factors, especially in translator education.
Area of Science:
- Artificial Intelligence in Education
- Human-Computer Interaction
- Translation Studies
Background:
- Large language models (LLMs) are increasingly used in translator education, but how students interact with AI feedback during tasks is understudied.
- Existing research lacks empirical data on the behavioral acceptance of AI-generated feedback in situated translation practice.
Purpose of the Study:
- To investigate translation students' behavioral acceptance of AI-generated feedback from an LLM-embedded platform.
- To examine the influence of baseline attitude toward AI, translation proficiency, and text genre on feedback acceptance.
- To develop a framework explaining the mechanisms behind AI feedback acceptance in translator education.
Main Methods:
- Mixed-methods study involving 124 translation majors providing 3596 sentence-level accept/reject decisions with rationales.
- Mixed-effects logistic regression to analyze factors predicting acceptance.
- Thematic analysis of written rationales to identify reasoning mechanisms.
Main Results:
- Text genre, baseline AI attitude, and translation proficiency significantly predicted feedback acceptance.
- Acceptance rates varied by genre (81.2% for technical, 49.9% for literary).
- Skeptical attitudes reduced acceptance; higher proficiency increased selectivity.
Conclusions:
- AI feedback acceptance is a hierarchical, conditional process involving cognitive, affective, and ethical considerations (Cognitive-Affective-Ethical framework).
- Findings advance a mechanism-based understanding of AI feedback acceptance beyond pre-adoption intention.
- Implications for genre-aware pedagogy and adaptive AI feedback system design in translator education.
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