BRIDGE: TRENDS AND TRADITIONS IN TRANSLATION AND INTERPRETING STUDIES

Koscelníková, Mária. 2024. Video Game Localisation in Slovakia. Nitra: Constantine the Philosopher University in Nitra.

Zuzana Hudáková — 2025-12-17

Kasperė, Ramunė; Horbačauskienė, Jolita; Liubinienė, Vilmantė; Motiejūnienė, Jurgita; Patašienė, Irena and Patašius, Martynas. 2024. Machine Translation and Society: The Case of Lithuania. Kaunas: Kaunas University of Technology.

Emilia Perez — 2025-12-17

Introduction: Toward human-centred AI in translation studies

Marián Kabát — 2025-12-17

Artificial intelligence (AI) is now woven into translation in ways that were scarcely imaginable a decade ago. Neural machine translation (NMT) built on the Transformer architecture (Vaswani et al., 2017) has altered expectations of speed, coverage and quality, while increasingly multimodal, massively multilingual systems promise “any-to-any” translation across text and speech. Yet if translation studies is to harness these advances responsibly, we need to place people (translators, interpreters, localizers, educators, communities, and end-users) at the center of system design, policy, and evaluation. This is the core proposition of human-centered AI (HCAI): design AI that amplifies human agency, mastery, and accountability, rather than displacing them (Shneiderman, 2022, for HCAI in translation studies see Jiménez-Crespo, 2025).

HCAI is not a vague ideal. In human–computer interaction (HCI), concrete guidance exists for how AI systems should behave in practice (e.g., the “Guidelines for Human-AI Interaction,” which articulate principles like keeping users in control, supporting efficient correction, and exposing system confidence). These guidelines are directly relevant to translation tools, from interactive MT to quality estimation dashboards and post-editing environments. At the same time, broader regulatory frameworks now impose obligations on providers and deployers of AI. In the European Union, for example, the AI Act entered into force on 1 August 2024 with phased applicability through 2025–2027, including near-term prohibitions and obligations for general-purpose AI and later rules for high-risk systems – developments with clear implications for translation workflows and vendors.

This issue on Bridge takes up the challenge of human-centered AI in translation by foregrounding translators’ expertise and lived practices, domain-specific quality, equity for under-resourced languages and modalities, and educational and regulatory transitions. Below is a sketch of the technological backdrop, and a map of key human-centered concerns.

The role of philosophy in translators' response to automation

Eno Ubong Ekpo — 2025-12-17

Through the ages philosophy has been used as a tool for the understanding of all that pertains to life and how things hang together. Technology, a salient part of human life in the 21st century, has not been left out of these philosophical postulations. From the inception of the philosophy of technology two centuries ago philosophers have focused largely on defining the meaning of technology as well as its influence on society and culture. This outlook is called the humanities’ philosophy of technology. This paper is an attempt to analyse the role of philosophy in the response of translators to automation or technology in their career as well as its role in translator education. The humanities’ philosophy of technology is analysed here by comparing three schools of thought concerned with it: Rapp’s socio-physical impact approach, Wartofsky’s particularistic and social impact approach as well as Mitcham’s humanities’ approach.

From unrestricted access to pencil and paper: Evaluating machine translation post-editing in translation classes

Tomáš Svoboda — 2025-12-17

The effort to adapt translation programmes to the rapid development of machine translation (MT) most commonly manifests itself in the introduction of new content into the curriculum, specifically machine translation post-editing (MTPE). Yet, students use available MT technology even in seminars focused on traditional (not MTPE) translation. Considering this development, a survey was carried out within the European Master’s in Translation network in autumn 2022. The research covered both actual instruction/ exams in translation courses and final exams. This article assesses the respondents’ answers (a quantitative approach) and reviews the comments of respondents (a qualitative approach). The results show that most translation seminars cover MTPE to a limited extent only. During instruction, the most common way to track MT use is for students to supply a commentary with their translations. The prevailing attitude towards MT in examinations involved no ban being imposed on MT usage or students were not discouraged from using it. If, however, MT was explicitly banned in the course, the ban was more likely to be enforced during exams rather than in actual instruction during seminars. The article concludes with an outlook of future developments predicting a more pronounced blending of MT use and MTPE in dedicated classes.

Dialogue on posthuman’s power: generative AI and the ethical rewriting in translated literature

Zhenhao Zhong — 2025-12-17

This study explores the dialogue between posthuman power and generative AI within the context of translation studies, building upon the Chinese translation of Kazuo Ishiguro’s Never Let Me Go by Kun Zhang. The novel presents a posthuman society where clones serve as organ donors for extending human lives. Zhang’s translation, Mo Shi Mo Wang (“莫失莫忘”), highlights the paradox of clones embodying humanity through discipline while humans transcend into posthumanism via organ transplantation. This paper delves into the translation’s focus on power dynamics by analyzing mechanisms of power discipline and the subversive assimilation of power, providing a detailed examination of how the text is rewritten within the Chinese sociocultural context. Through this lens, the study reveals how the translation underscores the inevitability of the clones’ tragic fate as a means to deepen the understanding of power structures in the original work. Furthermore, the integration of generative AI is considered as a contemporary manifestation of posthuman technology, engaging in the rewriting of power relationships and ethical dilemmas. The study argues that generative AI not only amplifies the exploration of power dynamics but also introduces a new layer of ethical considerations in the translator’s role and decision-making process.

Students’ perceptions and experience regarding the use of artificial intelligence in interpreter training

Miroslava Melicherčíková — 2025-12-17

The paper presents findings from a study investigating students’ attitudes toward and experience with AI tools in interpreter training. Data were collected through semi-structured interviews with 32 Master’s degree students. The results indicate widespread use of AI tools, such as ChatGPT, Gemini, Perplexity, and Consensus, for glossary creation, topic familiarization, and efficient preparation. Students generally perceived AI positively, recognizing its potential to streamline their work, though concerns were raised about possible misuse and a decline in critical thinking. Despite varying experience levels, AI use remained consistent, primarily influenced by the availability of relevant training materials. Students at different stages of training used AI in similar ways, differing mainly in the amount of time devoted to preparation. Students acknowledged AI’s increasing integration into interpreting practice, viewing it as a helpful aid rather than a replacement for human interpreters. The study advocates for the integration of AI into interpreter training, emphasizing contextual preparation and the development of technological competence. It also highlights the role of teachers in guiding students toward the effective use of artificial intelligence, fostering an innovative learning environment. Future research with larger and more diverse samples is recommended to further explore AI’s impact on interpreter training.

Digital innovation in EFL through inclusive AI: The University of Macerata case study within the UNITE project

Francesca Raffi — 2025-12-17

Artificial intelligence is transforming multiple domains of education, expanding opportunities for English language learning and teaching while also raising important questions about access, equity, and inclusion (Bahroun et al. 2023). Within this evolving landscape, the Universally Inclusive Technologies to Practice English (UNITE) project investigates how AI dialogue systems can be integrated into English as a Foreign Language (EFL) instruction when their use is guided by the Universal Design for Learning (UDL) framework (CAST 2024). The project aims to understand the pedagogical and technological conditions that enable chatbots to foster engagement, comprehension, and learner agency without introducing additional cognitive or emotional barriers (Fryer et al. 2019; Reinders and Benson 2017; Ushioda 2011; Dörnyei and Ryan, 2015).

This report focuses on the University of Macerata case study, which explores students’ experiences of chatbot-mediated interaction through a structured experimental design. Participants engaged in guided conversations with ChatGPT and Pi.ai, followed by a post-task questionnaire. The analysis concentrates on two key experiential dimensions - immersion in conversational flow and perceived quality of interaction - which closely align with UDL’s three principles of engagement, representation, and action/expression (CAST 2024). Among the project’s main outputs, the Chatbot Accessibility Checklist, developed by the University of Macerata research unit in collaboration with the Center for Applied Special Technology (CAST), the organization that originally designed the UDL framework. The checklist operationalizes these insights into UDL-based criteria for the pedagogical and technical integration of AI chatbots in higher education.