AI Tools and Translation Performance: Examining the Emotions and Anxiety of Postgraduate Translation Students

Abstract

The integration of artificial intelligence (AI) translation tools in translator education has fundamentally transformed pedagogical approaches, yet the psychological dimensions of this technological shift remain underexplored. This study investigates the emotional landscape and anxiety patterns among postgraduate translation students when utilizing AI-assisted translation tools. Drawing on cognitive load theory and the technology acceptance model, the study employed a mixed-methods design combining survey data from 187 MA translation students across 12 Chinese universities with semi-structured interviews and translation performance assessments. The findings reveal a paradoxical emotional profile: while students report heightened efficiency and reduced cognitive load during routine translation tasks, they simultaneously experience significant anxiety related to skill atrophy fears, academic integrity concerns, and professional identity displacement. Regression analysis indicates that AI anxiety negatively correlates with translation performance (β = -0.34, p < 0.001), while emotional engagement with AI tools positively predicts learning outcomes (β = 0.28, p < 0.01). The study contributes to translation pedagogy by proposing an emotion-regulated AI integration framework that addresses the psychological barriers to effective human-AI collaboration in translator training.