Human-centredness in translating with technology

In today’s rapidly evolving business landscape, Artificial Intelligence (AI), and specifically Large Language Models (LLMs), are redefining how organizations operate, make decisions, and engage with customers. AI-driven technologies have become indispensable, providing businesses with powerful tools to streamline operations, derive actionable insights from vast data, and foster more meaningful customer interactions. For business leaders, scholars, and practitioners alike, understanding the transformative potential of AI isn’t just advantageous—it’s essential to staying competitive in an increasingly data-driven world.This editorial delves into recent scholarly advancements in LLM applications within business contexts, analyzing studies that explore AI’s potential across various domains, from decision support to creative industries. By introducing a structured framework, this editorial highlights key insights and contributions from recent studies, assessing their value to academia and industry. The following comparative analysis sheds light on how these innovations shape our understanding of AI’s role in business while pointing to future research directions.Puyt and Madsen's (2024) study stands out as a foundational exploration of LLM accuracy, assessing ChatGPT-4's ability to recount the history of the SWOT analysis-a vital business strategy tool. Their findings reveal that, while ChatGPT-4 effectively conveys general concepts, it struggles with detailed historical information, often producing inaccuracies or "hallucinations." This gap underscores the need for LLMs to be trained with verified academic data, particularly for strategic business applications that demand precision. This study not only contributes to the literature by proposing methods to evaluate AI accuracy in historical contexts but also highlights the importance of rigorous information vetting in industry settings where reliability is crucial.In contrast, Raikov et al. (2024) explore a hybrid intelligence model that combines LLM capabilities with explainable AI (XAI) principles to enhance human-machine collaboration. Their approach emphasizes cognitive semantics, improving transparency and decision-making efficiency. The hybrid model's real-time adaptability addresses the needs of complex, regulated industries such as finance and healthcare, where trust in AI decisions is paramount. Academically, this study provides a valuable addition to XAI literature by demonstrating how LLMs can bridge the gap between AI autonomy and human oversight, making it a model for future human-AI interactions in complex business environments.Another significant study by Mariotti and colleagues (2024) examines the integration of LLMs with enterprise knowledge graphs to enhance data-driven decision-making. By enabling organizations to leverage knowledge graphs for more accurate and scalable data retrieval, this research provides a robust framework for businesses seeking efficient knowledge management systems. The academic contribution here lies in advancing the dialogue between LLMs and knowledge graphs, emphasizing ethical data handling and quality standards essential for industry applications. For enterprises, the study offers practical solutions to achieve streamlined data management, balancing automation with privacy and security. 2024) take a different approach, investigating LLMs' role in creative industries, specifically within fashion design. They introduce a hybrid intelligence model that supports creative processes, allowing AI to complement rather than replace human ingenuity. While LLMs in this field demonstrate potential in automating repetitive design tasks and enhancing customer personalization, the study reveals limitations in AI's ability to handle spatial and stylistic nuances. This study's academic contribution lies in promoting human-AI co-creation, inspiring further research into AI applications across diverse creative sectors, including media and marketing.Collectively, these studies not only illuminate LLMs' transformative potential in business but also highlight critical ethical and operational considerations. Ensuring accuracy, transparency, and data privacy are vital to responsibly integrating AI into business workflows. Future research should focus on enhancing LLM accuracy, refining hybrid intelligence models, and exploring creative AI applications, all while maintaining ethical standards. As LLMs evolve, interdisciplinary collaborations will be essential to harness their full potential, making AI an ethical, effective, and innovative force in the business world.

Introduction: Cross-Cultural Reading

This article presents a discussion of the theoretical and methodological challenges posed by digitization processes and born-digital sources within translation and translator studies research, particularly in archival contexts. It begins by demonstrating how the digitization of archives and source materials remains an undertheorized issue in translation contexts, as well as the need to understand how digitization allows new forms of “peripheral vision” across our research and publication processes. Subsequently, the article problematizes the archiving and use of born-digital sources to research translation and translators, particularly in terms of issues surrounding data collection, access and ethics. In doing so, this article argues that care must be taken to properly understand and preserve the digital sources that will enable archival research in the future, whilst emphasizing the new opportunities presented by digitized and born-digital sources to challenge the invisibility of translation and translators within many of the world’s archives and histories.

The notion of paratext is an unquestionably important consideration for many lines of research in translation studies: the history of translation, literary translation, audiovisual translation, and the analysis of ideological discourse in translation or self-translation. This inexplicable short-age of studies on paratexts in translations was one of the reasons why the Department of Translation and Interpreting at the Universitat Autònoma de Barcelona decided to organise the 7th International Conference on Translation in 2010, which on this occasion was organised under the title Paratextual Elements in Translation. Many of the ideas presented in this book date back to this conference, which served as a forum and platform for debate for all those researchers interested in the links between paratextuality and translation in their various forms and aspects

The high performance of generative artificial intelligence (AI) and large language models (LLM) in examination contexts has triggered an intense debate about their applications, effects and risks. What legal aspects need to be considered when using LLM in teaching and assessment? What possibilities do language models offer? Statutes and laws are used to assess the use of LLM: - University statutes, state higher education laws, licensing regulations for doctors - Copyright Act (UrhG) - General Data Protection Regulation (DGPR) - AI Regulation (EU AI Act) LLM and AI offer opportunities but require clear university frameworks. These should define legitimate uses and areas where use is prohibited. Cheating and plagiarism violate good scientific practice and copyright laws. Cheating is difficult to detect. Plagiarism by AI is possible. Users of the products are responsible. LLM are effective tools for generating exam questions. Nevertheless, careful review is necessary as even apparently high-quality products may contain errors. However, the risk of copyright infringement with AI-generated exam questions is low, as copyright law allows up to 15% of protected works to be used for teaching and exams. The grading of exam content is subject to higher education laws and regulations and the GDPR. Exclusively computer-based assessment without human review is not permitted. For high-risk applications in education, the EU's AI Regulation will apply in the future. When dealing with LLM in assessments, evaluation criteria for existing assessments can be adapted, as can assessment programmes, e.g. to reduce the motivation to cheat. LLM can also become the subject of the examination themselves. Teachers should undergo further training in AI and consider LLM as an addition.

BackgroundThe exponential growth of medical knowledge presents a paradox for modern medical education. While access to information is immediate, applying it in a clinically meaningful way remains a challenge. Large language models (LLMs), such as ChatGPT, are widely used for information retrieval, yet their role in dynamic, high-pressure clinical learning remains poorly understood.ObjectiveThis study aims to evaluate whether unstructured access to an LLM improves decision-making, teamwork, and confidence in trauma education for medical students.MethodsThis randomized controlled pilot study involved 41 final-year medical students participating in a trauma simulation session. Students self-selected into teams of 4 to 6 and were randomized to either an LLM-assisted group (ChatGPT-4o mini) or a control group without LLM access. All teams completed 18 video-based trauma scenarios requiring time-sensitive clinical decisions. Prompting was unrestricted. Confidence and trauma exposure were assessed using pre- or postquestionnaires. Facilitators rated teamwork (1-5), decision accuracy, and response times. Knowledge retention was measured 4 weeks later via an online quiz.ResultsConfidence in trauma management improved in both groups (P<.001), with larger gains in the non-LLM group (P=.02). LLM support did not enhance the decision accuracy or speed and was associated with longer response times in some complex cases. Teams without LLMs demonstrated more active discussion and scored higher in teamwork ratings (median 5.0 [IQR 5.0-5.0] vs median 3.5 [IQR 3.0-4.5]; P=.08). Students primarily used the LLM for fact-checking but reported vague or overly general responses. Knowledge retention was high across both groups and did not differ significantly (P=.33).ConclusionsWhile students appreciated the inclusion of artificial intelligence (AI), unstructured LLM use did not improve performance and may have disrupted the group reasoning. The use of non-English prompting likely contributed to lower AI performance, underscoring the importance of language alignment in LLM applications. This pilot study highlights the need for structured AI integration and targeted instruction in AI literacy. Simulation-based trauma education proved effective and well received, but optimizing the educational value of LLMs will require thoughtful curricular design. Further studies with more students are needed to define best practices for LLM use in clinical education.

With the rapid evolution of artificial intelligence (AI), its potential implications for higher education have become a focal point of interest. This study delves into the capabilities of AI in physics education and offers actionable AI policy recommendations. Using openAI’s flagship gpt-3.5-turbo large language model (LLM), we assessed its ability to answer 1337 physics exam questions spanning general certificate of secondary education (GCSE), A-Level, and introductory university curricula. We employed various AI prompting techniques: Zero Shot, in context learning, and confirmatory checking, which merges chain of thought reasoning with reflection. The proficiency of gpt-3.5-turbo varied across academic levels: it scored an average of 83.4% on GCSE, 63.8% on A-Level, and 37.4% on university-level questions, with an overall average of 59.9% using the most effective prompting technique. In a separate test, the LLM’s accuracy on 5000 mathematical operations was found to be 45.2%. When evaluated as a marking tool, the LLM’s concordance with human markers averaged at 50.8%, with notable inaccuracies in marking straightforward questions, like multiple-choice. Given these results, our recommendations underscore caution: while current LLMs can consistently perform well on physics questions at earlier educational stages, their efficacy diminishes with advanced content and complex calculations. LLM outputs often showcase novel methods not in the syllabus, excessive verbosity, and miscalculations in basic arithmetic. This suggests that at university, there’s no substantial threat from LLMs for non-invigilated physics questions. However, given the LLMs’ considerable proficiency in writing physics essays and coding abilities, non-invigilated examinations of these skills in physics are highly vulnerable to automated completion by LLMs. This vulnerability also extends to pysics questions pitched at lower academic levels. It is thus recommended that educators be transparent about LLM capabilities with their students, while emphasizing caution against overreliance on their output due to its tendency to sound plausible but be incorrect.

This chapter discusses the implication of corpus-based translation studies as a new research methodology in translation studies, which expands the scope of translation studies. The chapter then analyzes factors to be investigated in corpus-based translation studies following an analysis of the problems that arise. Despite all the limitations, corpus-based translation studies promise to be a worthy field for research in translation studies.

As generative artificial intelligence (AI) tools and large language models (LLMs)-powered applications develop rapidly in the era of algorithms, it should be integrated thoughtfully to enhance English as a Foreign Language (EFL) teaching and learning without replacing learners’ critical thinking (CT). This study systematically analyzes the impact of generative AI tools and LLMs on language learners’ CT in EFL education using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to identify, evaluate, and synthesize relevant studies from 2022 to 2025. A thorough review of 15 selected studies focuses on generative AI tools and LLMs’ dual nature, research methods, main focuses, theory and models, limitations and challenges, and future directions in the field based on Web of Science (WoS), SCOPUS, ERIC, ProQuest, and Google Scholar. The findings identified generative AI tools and LLMs possessed both the potential to nurture and the risk of hindering CT in EFL education. 66.67% of studies reported generative AI tools and LLMs’ positive role in CT, while 33.33% of studies reported its negative role in CT. Furthermore, 3 types of research methods, 3 key themes of research focus, and 4 groups of theoretical perspectives were examined. However, 4 kinds of limitations in this field remain, including research scope, user dependency, generative AI reliability, and pedagogical integration. Future research can focus on assessing long-term effects, broadening research scope, promoting responsible AI use, and refining pedagogical strategies. Finally, Limitations, implications and future direction of this study were discussed.

Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Non-Custom Models for Evidence-Based Medicine

Background:Artificial intelligence (AI) and large language models (LLMs) are increasingly integrated into healthcare, yet their adoption in pediatric urology remains insufficiently explored. Pediatric urology, with its complex and rare conditions, may particularly benefit from AI-based innovations.Objectives:This study aimed to assess pediatric urologists’ awareness, usage patterns, and perceptions regarding AI and LLMs, while also identifying potential applications, barriers, and educational needs.Design:A cross-sectional, descriptive survey was conducted among pediatric urologists.Methods:Between May and July 2025, a 21-item questionnaire was distributed via professional networks and mailing lists. Items addressed demographics, knowledge of AI, frequency and purpose of AI and LLM use, perceived clinical and surgical applications, ethical concerns, and interest in AI training. Descriptive statistics were used for analysis.Results:Of 368 invited pediatric urologists, 103 (28%) responded. Most reported moderate (35.0%) or low (29.1%) knowledge of AI, yet more than half (51.5%) used AI tools daily. LLMs had been used by 96.1% of participants, mainly for scientific writing (78.8%), language editing (54.5%), and text summarization (46.5%). Surgical simulation (46.6%), imaging-based strategy planning (40.8%), and preoperative planning (36.9%) were identified as promising clinical applications. Barriers included lack of trust (52.4%), ethical concerns (43.7%), and insufficient knowledge (35.0%). A strong interest in structured AI training was expressed by 81% of participants. Although responses were obtained from multiple countries, the majority of participants were based in Turkey, and the findings should be interpreted accordingly.Conclusion:Pediatric urologists demonstrate substantial engagement with AI in academic work, while clinical integration is still limited. The findings highlight a strong demand for AI education and emphasize the need for regulatory clarity, ethical frameworks, and validated tools to enable safe and effective use of AI in pediatric urology.

The modern stage of the world development can be characterized by a significant increase in the role of science and technology, in connection with which the problem of translating scientific and technical texts is becoming more and more urgent. That is why, among the important problems of modern translation studies, the development of such a field as the translation of scientific and technical texts occupies an important place, since the flow of scientific information, the exchange of experience between specialists of various fields, and the organization of international symposia and conferences require the active work of qualified translators. An essential feature of the language of scientific and technical texts, which distinguishes them from colloquial language or the language of fiction, is the use of certain lexical, grammatical and stylistic techniques. Translation of literature in this direction requires special skills and abilities, as it is necessary to convey the accuracy and brevity of the expression of thought, on the one hand, and preserve strict logical consistency and completeness of expression on the other. Among the factors that make scientific and technical translation from English into Ukrainian difficult, it is necessary to include differences in the grammatical systems of languages, which necessitates mastering certain translation transformations, orienting oneself in ways and methods of translating unique constructions. Difficulties in translation include the presence of ambiguous terms, grammatical homonymy in the original language, preservation of genre and style features during translation, which vary depending on the type of text (a monograph, an article from a special magazine, a technical description, a patent, a technical reference book). Such researchers as V. I. Karaban and I. V. Korunets were engaged in studying the peculiarities and problems of translation of scientific and technical literature. But, unfortunately, the degree of study of the proposed topic is insufficient, since the techniques and methods of translation of the accompanying technical documentation have not been fully explored [5], [7]. The relevance of our research is due to the interest of scientists and translators in the problem of scientific and technical translation, increasing the significance of the translation of scientific and technical literature as a way of exchanging and spreading information in the world. In addition, the study of the main issues of translation of scientific and technical literature makes it possible to master the techniques, means and methods necessary to achieve an adequate, equivalent translation.

Harnessing artificial intelligence in bariatric surgery: comparative analysis of ChatGPT-4, Bing, and Bard in generating clinician-level bariatric surgery recommendations

Having presented directions of development in Translation Studies, based on themes of subsequent European Society for Translation Studies Congresses; as well as the chronology of changes and turns in translation research, the author presents the Action Research in Translation Studies (ARTS) model, which combines functionalist theories in TS with translation practice. ARTS aims at using theoretical cogitation in TS in order to introduce specific translation activities. The application of the ARTS model is illustrated here by the analysis of the Katzenjammer Kids translation unit. The conclusions regard the translator’s decision process and the influence of cultural conditioning on the creation of meaning in translation.

The last decade has seen rapid technological progress, which has opened up new research perspectives for language and translation studies, and extended empirical boundaries. Eye-tracking research is still considered to be innovative. The author emphasizes the need to intensify empirical research in translation studies, including eye-tracking research studies, and then describes their application by formulating specific research questions. This approach allows a description of real potentialities and perspectives of the eye-tracking method.In this paper the author describes an eye-tracking study which focused on the process of sight translation. The study was carried out using an eye-tracker Tobii T120. The author presents the results of eye-tracking analyses of sight translation of a Polish text into Russian and focuses on aspects of lapsology and translation didactics. By using heat maps and gazeplot maps obtained the author selects the text elements that required longest and most frequent fixations among the subjects and were thus indicators of translation problems. These elements were then subjected to a detailed translation analysis.The data obtained allows the author to indicate translation problem areas and strategies applied by the subjects. The next step in a comprehensive and systematic eye-tracking analysis of a sight translation will be to offer practical recommendations in the form of a specific translation teaching curriculum.