What Drives AI Tool Adoption in Translation Practice? A TAM-Based Study of Translation Majors
DOI:
https://doi.org/10.54855/callej.262725Keywords:
Technology Acceptance Model, AI tools, translation education, perception, translation scoresAbstract
Despite the increasing integration of AI tools in translation training, limited research has examined how Technology Acceptance Model (TAM) factors influence students’ adoption and the relationship between TAM and learning outcomes. This study applies the TAM to examine the determinants of AI tool adoption in translation practice among 59 senior translation majors in Vietnam. The study employed an explanatory sequential mixed-method design using close and open-ended questionnaire items on its four components (Perceived Usefulness - PU, Perceived Ease of Use - PEOU, Attitude toward Use - ATU, and Behavioral Intention - BI), and the sets of students’ performance scores in AI-related translation technology and translation practice courses. The findings revealed translation majors’ general positive perceptions. PEOU and PU got higher mean scores while ATU got the lowest, with more variation. However, ATU was the strongest predictor of future use, followed by PEOU and PU. Relationships between TAM components and performance scores are weak or non-significant, indicating limited predictive value for academic outcomes. The TAM component analysis also resulted in near-zero correlations with the scores. Thus, AI tools are perceived as useful and accessible in translation practice, but their adoption alone does not mean better performance scores, highlighting the need for guided pedagogical integration applicable to Vietnam’s context and similar ones.
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