Understanding Students' Acceptance of ChatGPT as a Translation Tool: A UTAUT Model Analysis

TL;DR

This study applies the UTAUT model to understand factors influencing students' acceptance of ChatGPT as a translation tool, highlighting differences based on experience and training, and offering insights for integrating AI into translation education.

Contribution

It extends the UTAUT framework to analyze ChatGPT adoption in translation training, considering experience and training as moderating factors, with empirical data from university students.

Findings

Social influence more impacts less-experienced users' intentions.

Facilitating conditions more influence non-translation students.

Different factors drive translation vs. non-translation students' use.

Abstract

The potential of ChatGPT to transform the education landscape is drawing increasing attention. With its translation-related capabilities being tested and examined, ChatGPT presents both opportunities and challenges for translation training. The effective integration of ChatGPT into translation training necessitates an understanding of students' reactions to and acceptance of ChatGPT-assisted translation. Against this backdrop, this study draws on the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the potential determinants of students' adoption of ChatGPT for translation and investigates the moderating effects of use experience and translation training on those relationships. An online survey targeting university students in Hong Kong collected 308 valid responses, including 148 from translation students and 160 from non-translation students. Respondents were divided into two groups based on their ChatGPT use experience. Data were analyzed using structural equation modeling. A multigroup analysis revealed different structural relationships between the influencing factors of students' intention to use ChatGPT across groups. Notably, less-experienced users' behavioral intention to use ChatGPT for translation was more strongly correlated with social influence compared with experienced users. Non-translation students' use intention was more strongly driven by facilitating conditions compared to translation majors. These results are discussed with the different primary purposes of translation and non-translation students' translation practices. The findings of this study contribute to the growing body of research on AI-powered translation training and provide insights for the ongoing adaptation of translation training programs.