What drives university students to use ChatGPT for translation? Disciplinary and experiential influences

Abstract

The increasing use of large language models like ChatGPT has sparked interest in their potential for translation tasks. However, little is known about what drives university students to adopt these tools or how disciplinary background and prior experience shape their decisions. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), this study explores the adoption of ChatGPT for translation tasks among university students in Hong Kong. Survey responses from 308 students, including translation and non-translation majors, were analyzed using structural equation modeling. Results show that performance expectancy is the strongest determinant of adoption intention, followed by facilitating conditions, while effort expectancy and social influence were less significant. Experience level emerged as an important moderating factor: novice users relied on both social influence and performance expectations, whereas experienced users prioritized performance alone. Disciplinary differences were also pronounced. Translation students primarily valued performance benefits and used their technical expertise to evaluate ChatGPT independently. Non-translation students, however, were influenced by both performance expectations and facilitating conditions, suggesting a greater need for institutional support. These findings highlight the importance of tailored educational approaches that address the specific motivations of different student populations. For translation students, this means emphasizing advanced features and critical evaluation, while for non-translation students, it involves providing stronger support systems and guidance. The study also offers insights for LLM developers, underscoring the need for user-centered design that accommodates the diverse needs, experiences, and expectations of different student groups. ChatGPT等大语言模型日益普及, 使其在翻译领域的应用潜力备受学界和业界关注。然而, 关于大学生为何使用此类工具, 以及学科背景与ChatGPT使用经验如何影响决策, 尚缺乏系统研究。本研究以整合技术接受模型 (UTAUT) 为框架, 调查香港地区308名大学生 (涵盖翻译与非翻译专业) 在翻译任务中使用ChatGPT的动因, 并采用结构方程模型进行分析。研究结果表明, 绩效期望是影响行为意向的最关键因素, 便利条件次之;努力期望与社会影响的作用则相对较小。进一步分析显示, 使用经验是重要的调节因素, 新手的行为意向受到社会影响与绩效期望的共同驱动, 而有经验的用户则更看重绩效因素。学科背景的差异同样显著, 翻译专业学生更重视ChatGPT的任务表现且更倾向于自行评估;相比之下, 非翻译专业学生不仅关注任务表现, 还受到便利条件影响, 因此更需要院校支持。研究结果凸显了教育策略须根据学生动因的差异作精准调整, 对翻译专业可强化高阶功能与批判性评估训练, 对非翻译专业则完善配套支持与使用指导。此外, 本研究也为大型语言模型的开发者提供了重要启示, 即应秉承以用户为中心的设计理念, 充分考虑不同学生群体的多元化需求、经验与期望。