Prompt engineering in translation: How do student translators leverage G en AI tools for translation tasks (2025)

Abstract: GenAI, though not developed specifically for translation, has shown the potential to produce translations as good as, if not better than, contemporary neural machine translation systems. In the context of tertiary-level translator education, the integration of GenAI has renewed debate in curricula and pedagogy. Despite divergent opinions among educators, it is evident that translation students, like many other students, are using GenAI tools to facilitate translation tasks as they use MT tools. We thus argue for the benefits of guiding students in using GenAI in an informed, critical, and ethical manner. To provide insights for tailored curriculum and pedagogy, it is insightful to investigate what students use GenAI for and how they use it. This study is among the first to investigate translation students’ prompting behaviours. For thematic and discourse analysis, we collected prompts in GenAI tools generated by a representative sample of postgraduate student participants for eight months. The findings revealed that students had indeed used GenAI in various translation tasks, but their prompting behaviours were intuitive and uninformed. Our findings suggest an urgent need for translation educators to consider students’ agency and critical engagement with GenAI tools.

## generative ai and translation ai has gradually permeated our life and work over the past two years. in particular, the launch of chatgpt in 2022 captured significant attention across various sectors with its unprecedented ability to generate contextually relevant responses based on pattern recognition. since then, chatgpt and other generative ai (genai) tools have experienced rapid development and continued to attract public attention. genai tools have now been embedded in our smartphones and laptops with great utility. despite their limitations, genai tools are also said to have significantly transformed our work and the industries at large by improving automation, efficiency and productivity @xcite . in the industry and discipline of translation and interpreting, genai has also been experimented with and adopted by language service providers and professional translators. though not specifically developed for translation, genai has been applied to converting texts from one language to another, given the training data and neural network architecture similarities between genai and neural machine translation (nmt). both genai and nmt rely on natural language processing and transformer-based models. genai has shown the potential to generate translations of quality equal to, if not superior to, that of contemporary nmt @xcite . thus, we argue that genai tools can be considered a broader form of mt and language tools. however, automatically translating from one language into another is merely one of genai's many functions. beyond automatic translation, genai has been instrumental in facilitating the entire translation process, from background information searching and translation strategy analysis to proofreading and editing. consequently, there is a growing trend among professionals to integrate genai into translation workflows, exploring innovative ways to enhance translation productivity and quality. indeed, the role of genai tools, especially chatgpt, in empowering human translators has been discussed and researched in the last two years. studies have shown that genai offers advantages over human translators in terms of efficiency in processing lengthy text, accuracy in terminology translation, and consistency in style (e.g., @xcite @xcite . when collaborating with human translators, genai models have outperformed contemporary nmt models (e.g., google translate) in enhancing translation quality by integrating pre-editing analyses and interactive inputs (e.g., @xcite . while genai has proven effective in assisting translation practices, it also exhibits significant shortcomings, such as accuracy issues (e.g., mistranslations from limited contextual understanding) @xcite , creativity constraints (e.g., failure to produce nuanced and culturally resonant translations) @xcite , and ethical concerns (e.g., perpetuation of biases in training data) @xcite . addressing these challenges requires human discretion in critically evaluating ai outputs @xcite . ## genai and translator training in the context of tertiary-level translator education, the integration of genai has renewed previous debates on the benefits and challenges of integrating translation technologies, particularly machine translation, into our curricula (e.g., see @xcite @xcite . on the one hand, the integration of genai tools into translator education has been advocated, given its benefits @xcite , which have been explored in previous studies, including improving bilingual and extra-linguistic competencies and enhancing translation efficiency (e.g., @xcite . on the other hand, the inappropriate integration of genai into translator education could adversely affect the development of students' translation competence. given the current limitations of genai-generated translations, students must acquire critical skills to evaluate and refine these outputs. however, translation students' overreliance on genai during the learning process may raise concerns about the non-critical evaluation and use of its outputs @xcite . however, translation students' overreliance on genai during the learning process may raise concerns about the non-critical evaluation and use of its outputs @xcite . regardless of the debate concerning the integration of genai in translator training, the lack of empirical studies means that most discussions and decisions about genai in translator training are experiential and intuitive. so far, the integration of genai in translator training has been extensively discussed, mainly in theoretical literature. scholars tend to focus on how technology impacts translator training and what transformation is needed for translation programs (e.g., @xcite . there is further discussion on how genai can be leveraged to teach translation and technology. however, relevant empirical studies are scarce, with only a handful of survey-based studies investigating students' and teachers' perceptions of ai in translation (e.g., łukasik, 2024; @xcite . evidence regarding students' interaction with genai, such as their prompting strategies, or the effect of teaching with genai, has yet to be found. indeed, these issues in the debate regarding integrating ai in translator training have existed long since the advent of mt some decades ago. genai has only caught the attention of researchers for around two years, so the number of studies is naturally still limited. while empirical studies on the integration of genai in translator training remain limited in number, existing research on mt has already shown the advantages and disadvantages of incorporating automatic translation in training (e.g., @xcite . given that students are likely to independently explore and experiment with genai, just as they did with mt @xcite , it is more beneficial to openly discuss these tools rather than prohibiting discussion and access in the translation classroom. we thus argue that it would be better to understand how students have been using genai in translation tasks and provide tailored and essential guidance for them to leverage these tools. the first step in providing such tailored instructions is understanding students' usage of genai tools. ## prompt engineering while empirical studies on students' interaction with genai are scarce, prompt engineering has emerged as a specialised technique applied across other fields, such as computational linguistics, healthcare and education @xcite @xcite . this technique involves designing, refining, and implementing prompts (i.e., human input instructions) to optimise the output of genai to generate more accurate and contextually appropriate responses @xcite . prompt engineering frameworks have gradually emerged to guide practice. for example, the perfect framework focuses on key elements, including prioritising precision to reduce ambiguity, engagement to make prompts relevant, relevance to align with the task, flexibility to allow varied responses, efficiency to optimise resources, clarity for understanding, and iterative testing to refine prompts @xcite . however, such studies rarely focus on translation-specific challenges. recently, the knowledge of prompt engineering has been transferred to and explored in the translation field by comparing translation quality: studies that compare zero-shot and few-shot strategies (i.e., providing genai with no examples or a small number of examples to guide its response) have primarily focused on sentence-level translation and often overlooked the context (e.g., @xcite ; the level of input text have been considered in studies showing that full-document input yields better translation quality than sentence-by-sentence or multisentence block input (e.g., @xcite , but the prompting strategies examined do not apply to real-world translation practice that considers functionalist principles, such as target audience and translation purpose @xcite . to our knowledge, only one study has provided a human-like prompting framework for translation, which includes four key components: m for maps (keywords and terms), a for audience (tone and style), p for purpose (goal and context), and s for style (maintaining consistency and cultural adaptation @xcite . however, this framework does not provide clear definitions of these translation terms, and it appears to be derived from experiential insights rather than from translation practice or established theoretical frameworks. as such, its potential applicability to professional translation contexts calls for further exploration and validation in authentic translation settings. against this backdrop, there is a need for a more systematic framework that is grounded in realworld translation practice and supported by empirical data, whether for guiding human-genai translation practice or students in using genai in an informed manner. this study, therefore, aims to understand translation students' usage of genai tools by analysing their associated prompts. we intend to address the following research questions (rqs): to answer these rqs, we recruited 15 postgraduate students and collected their dialogues with genai tools over eight months for thematic and discourse analysis. the potential significance of this research lies in two key areas. firstly, the findings of this research are expected to provide empirical evidence regarding how translation students interact with genai, particularly how they formulate and use prompts. secondly, from a practical perspective, the findings could inform the development of effective pedagogical approaches for integrating genai into translator education. ## concluding remarks to answer the research questions posed in the current study, we collected and analysed student participants' prompts to explore their interaction with genai in translation tasks. our findings revealed that the student participants interacted with genai across various tasks, especially transfer and revision, in the translation process, even without proper training. in terms of discourse features, it is common that student participants' interactions with ai ended after a single round, lacking necessary iterative feedback and refinement, with prompts reflecting an informal, spoken language style. analysis of sentence structures and word choices further revealed the student participants' diverse prompting strategies, shaped by their language use. regarding the content of the prompts, the findings indicate a lack of awareness in incorporating contextual cues, which may limit the effectiveness of genai in generating appropriate translations. it was evident that the student participants applied translation theories to their prompts, demonstrating an understanding of translation concepts and quality criteria; however, their use of vague, abstract terms may introduce ambiguity, leading to less accurate ai outputs. overall, these interactions provide valuable insights into how genai can be integrated to improve educational interventions and professional practice. our findings can serve as references for designing specialised prompt engineering training for translation students, practitioners' professional development, and future studies analysing the products of student-genai interactions. our findings also suggest that these future translators increasingly rely on human-ai collaboration, thus posing new challenges for educators to urgently review translation education and adapt to this rapidly evolving landscape.