Translation competence in the age of generative AI: Debates, dilemmas, directions

Chapter 1 Translation competence in the age of generative AI: Debates, dilemmas, directions Gary Masseya& Maureen Ehrensberger-Dowa aZHAW Zurich University of Applied Sciences, Switzerland (ret.) Until recently, the description and modelling of the competences and skills needed to translate successfully, and the ways in which they develop, have seen a steady evolution and predictable expansion, largely to accommodate technological advances and an increasing awareness of situatedness. However, the impact of neural MT and, now, generative AI (GenAI) has been unprecedented in rapidly transforming the core tasks of translators. Together with a proliferation of creative roles in a diversifying language industry, the volatility indicates a paradigm shift that is beginning to supplant even the once stable epithet “translator”. It also questions the efficacy of current descriptions of skills and confronts educators with dilemmas of balancing specialisation and generalisation, routinisation and adaptivity, core and transferable skills. This chapter considers relevant aspects of modelling competences and their development and examines related debates and dilemmas engaging educators and employers in the current and foreseen language-industry climate. It outlines directions for training in the age of translating with(out) GenAI, proposing an approach that, alongside core textual, interlingual translation and digital skills, combines transferable skills with human-machine/human-agent interaction (HMI/HAI) competence. Gary Massey & Maureen Ehrensberger-Dow. 2026. Translation competence in the age of generative AI: Debates, dilemmas, directions. In JC Penet, Joss Moorkens & Masaru Yamada (eds.), Teaching translation in the age of generative AI: New paradigm, new learning?, 3–26. Berlin: Language Science Press. DOI: 10.5281/zenodo.17641064 Gary Massey & Maureen Ehrensberger-Dow 1 Introduction 1.1 General What elements constitute translation competence (TC)1and how they can be developed have represented pivotal issues in applied Translation Studies since its inception (Holmes 1988: 77). They continue to trigger considerable debate amongst researchers, educators and practitioners, and they present educators and their organisations with dilemmas about how and where to target resources. In this chapter, we explore some debates and dilemmas triggered by changes to professional and educational practices brought about by generative artificial intelligence (GenAI). We also outline potential directions for human agents in the age of translating with and without GenAI. In our view, this necessitates an approach combining transferable skills with human-machine/human-agent interaction (HMI/HAI) competence, alongside “old-school” translation skills. Before proceeding to the debates, dilemmas and directions, however, we should define, describe and demarcate what exactly we mean by professional interlingual TC in the age of GenAI. 1.2 Definitions, descriptions and demarcation The word interlingual is used in Translation Studies and the language industry to designate a particular type of language mediation activity, namely that which takes place between natural languages and the particular cultures they represent. Alongside interpreting, translation represents a prototypical form of professional interlingual language mediation. In a language industry characterised by rapidly diversifying job titles and tasks (Bond 2018, Slator 2020: 11–17), the prototypical conceptualisation of what so-called (professional) translators do, i.e. translate content written in a source-language (SL) document into a target-language (TL) document, appears increasingly outmoded. Language professionals adaptively engage in a whole range of service provision as the lines between core and adjacent services blur (Angelone 2023, Angelone et al. 2024: 3–5). The major driver behind this state of affairs has been the technologisation of the industry as language service providers (LSPs) operate more and more downstream and upstream of previously core translation, localisation and interpret1This chapter applies the definitions of “skill” and “competence” from the latest EMT (2022: 3) competence framework. A skill is the “ability to apply knowledge and use know-how to complete tasks and solve problems”, while competence refers to “the proven ability to use knowledge, social and/or methodological abilities, in work or study situations and in professional and personal development”. 4 1 Translation competence in the age of generative AI ing activities. Indeed, the industry itself prefers the generic term linguist, and with good reason. Since 2016, the higher accuracy and fluency of neural machine translation (NMT) engines have led to more extensive automation of the translation process. Linguists have thus seen more deployment downstream of language transfer (or conversion) per se in post-editing (PE), output evaluation and quality assurance tasks. Most recently, the introduction of automated MT quality estimation (QE) to determine how much human PE is required is itself replacing more costly human MT quality evaluation (Slator 2024: 50–51). Upstream of transfer proper, linguists have been expanding their repertoire to perform tasks that include linguistic consultancy, MT pre-editing and multilingual content creation, for example in marketing, PR and corporate communications (often referred to as transcreation). Nevertheless, we shall retain the term translation, both in line with the title of the present volume and in recognition of the convincing argument made by do Carmo & Moorkens (2021) that the ultimate ethos of translation is to help communication flow between humans, regardless of the new technologies deployed and of the roles and activities these necessitate. GenAI has now entered the technological mix, and its applications clearly go far beyond interlingual mediation. Opportunities and risks have been identified in higher education in general (Atlas 2023, Gimpel et al. 2023, OECD 2023), where the potential of GenAI as an educational tool extends to teachers and students alike. Properly used, it can support course design, materials development, assessment, text and image production, coding, critical thinking and individualised learning. Improperly used, it can facilitate academic dishonesty, increase technological dependency, atrophy human skills and agency, undermine data protection and intellectual property rights, reinforce discrimination (due to data bias) and increase inequality (due to unequal access). Within the narrower confines of interlingual translation, GenAI differs decisively from AI-based language technology such as NMT in being capable of generating multilingual text, images and other content in response to prompts, including the prompt to translate. These prompts elicit statistically probable output generated by deep-learning models able to process and generate natural languages, known as large language models (LLMs). The prompt-response cycle can be iterative, allowing users to ask for more information or to provide more detailed prompts should the results require improvement (Gimpel et al. 2023: 22). Adequately structuring the instructions that prompt a GenAI model – also known as prompt design or, at the more technical level, prompt engineering – thus becomes central to its effective use. The skills and competences (see Footnote 1) necessary to embrace GenAI in the education and practice of professional 5 Gary Massey & Maureen Ehrensberger-Dow translators thus differ in major respects from those needed to master CAT tools and other translation technologies. It is indisputable that such skills and competences are essential to a language industry already transitioning from NMT to GenAI. Unbabel co-founder and CEO João Graça points out that LLMs are attracting more research and development, and can handle more complex tasks than NMT, such as automatic PE, source correction and cultural adaptation.2Lionbridge, one of the world’s leading LSPs, claims GenAI is “taking automated translation and localisation to new heights”.3Slator (2024: 52) reports that translation management systems (TMS) providers are already integrating GPT models to leverage LLMs’ capacity to rephrase, summarise and suggest new translations. LLMs can be used to incorporate QE into workflows and gauge PE effort, do PE or even provide linguistic insights throughout the translation workflow. Slator’s results also show two-thirds of MT providers offering fine-tuned LLMs and over 80% offering MT-LLM hybrid solutions. LSPs have the potential to become major providers and enablers of multilingual, multimodal GenAI content through content creation, validation, localisation, transmission and management. In keeping with the broadening portfolios of LSPs, multilingual text generation is currently “the most in-demand language AI application, after machine translation” (Slator 2024: 74). Fine-tuning and prompting LLMs to create and translate/localise content are emerging activities for language professionals to complement longer-established PE and quality assurance roles. Competent – or ideally expert – leverage of GenAI is a must for all those aiming to work in the language industry. But which skills and competences does and will this require? And in a dawning age of (language) work increasingly dominated by AI, what value can human intelligence and skills bring to bear? It is time to consider the debates around professional interlingual translation and the dilemmas that educators are facing. 2 Debates and dilemmas 2.1 Agency Asscher (2023) raised the issue of whether MT can actually be regarded as a proper point of interest in Translation Studies from a definitional perspective. He concludes that it is indeed compatible with both prescriptive, equivalenceoriented definitions of what translation is as well as descriptive definitions based 2Slatorpod #216, 12 July 2024 (https://www.youtube.com/watch?v=6smTEp3CXwQ) 3https://www.lionbridge.com/generative-ai/ 6 1 Translation competence in the age of generative AI largely on how translations are received. But he also points out that the perceived threat of AI to the social and professional status of translators, and of those who educate them (see also ELIS 2024: 24–25), may well redraw definitional boundaries and distinguish between MT and human translation on the basis of the perceived creative and moral authenticity of conscious agency (Asscher 2023: 14–16). Asscher’s argument draws on current positions on AI from the broader humanities and social sciences, such as moral philosophy (e.g. Sebastián & RudyHiller 2021). However, there is evidence of similar preoccupations in education policy and science, with emphasis falling on the importance of human agency, including teacher agency, and associated transferable skills – responsible action, critical thinking, systems thinking, logical reasoning, cultural agility, problem solving and emotion regulation (Gimpel et al. 2023, OECD 2023). Within the narrower confines of Translation Studies, it appears that agency is already emerging as the fulcrum around which the value of professional human translation can be measured. For example, Vieira (2020: 327–329) presents PE as a spectrum ranging from an MT-centred process typified by automatic PE to human-centred PE where humans have full control of output. Human agency moves back and forth along the spectrum as client, commission and employer demands require. The human-centred pole is more palatable to professional translators (Vieira 2020: 327), whose resistance to PE seems to derive from anxiety over a perceived loss of agency (Cadwell et al. 2018, Sakamoto 2019). In the discourse surrounding PE, agency has thus become a touchstone of professionalism and professional self-concept. Similarly, Rico & González Pastor (2022: 188) found in their pre-GenAI study of attitudes to teaching MT that the translation educators made much of the “human factor”. Though the data are unclear on precisely where they believe that agency resides, the participants place the translator at the centre of translation production. Interestingly, this prompts the researchers to posit that “MT has overcome its condition of tool”, and to claim that teaching MT should adopt a holistic approach “beyond an instrumentalist agenda that concentrates on the technical properties of the technologies” and which “evidences how the human factor is key to the translation process” (Rico & González Pastor 2022: 190, 193). Referring to other research, they also tentatively consider whether complementary effects of cognitive impairment or augmentation could influence competence development (Rico & González Pastor 2022: 192). One wider implication is that, even before the advent of GenAI, the paradigmatic human agency of the tool-user is being subverted by a more discerning and subtle appreciation of the rapidly evolving interactivity between translation technologies and their users. 7 Gary Massey & Maureen Ehrensberger-Dow The changed nature of that relationship appears to be reflected in the metaphors used to describe GenAI systems: ChatGPT and similar systems are referred to as “conversational agents” (Moorkens & Guerberof Arenas 2024: 75), and educational sources describe them as a “language partner”, “writing partner”, “writing collaborator”, “co-partner to formulate text”, “learning partner”, “intellectual sparring partner”, or “partner to generate codes” (e.g. Atlas 2023: ii: 64, Gimpel et al. 2023: 19–24). Business consultants and IT case researchers alike refer to human-GenAI interaction as “co-creation” (e.g. Eapen et al. 2023, Nah et al. 2023: 296).4 GenAI has now advanced well beyond the material agents in the “dance of agency” so deftly explored by Olohan (2011: 342) in interactions between translators and TM. This is a question not just of degree (speed, accuracy and adequacy of suggestions, etc.) but also, and most pertinently, of quality (reciprocal interactions and learning). The model pivotal to Olohan’s (2011: 344) conceptualisation of agency decisively distinguishes between the capacity of human agents to both resist and accommodate to technologies and of non-human agents to simply resist. But GenAI is patently capable of accommodation in its own right (deep learning). This renders irrelevant the perceived divide between the poles of social and technological determinism that Olohan (2011: 345) observes among translators commenting on TM, and which O’Brien (2024) criticises in her call to overcome the antagonism between technology and translators by focusing on a human-centred AI (HCAI) that amplifies rather than emulates human abilities (cf. Shneiderman 2020). As Risku & Windhager (2013: 36–37) point out, any technology or artefact used by translators in their work aligns with the concept of non-human “actants” in actor-network theory (ANT, e.g. Latour 2005). GenAI, however, quite obviously goes further in fulfilling the conditions of what the human-machine interaction (HMI) sub-field of human-agent interaction (HAI) has called “joint activity” (Bradshaw et al. 2011: 288): “the essence of joint activity is interdependence […] to produce something that is a genuine joint product”. Together with other effects of (Gen)AI on the processes and practices of professional translation, the agency issue yields multiple potential ramifications for the development and exercise of professional TC. It is to these that the next sections turn. 4The anthropomorphism is more than a conceit. Research on conversational agents used as social companions shows that human-AI friendship is similar to human-human relationships and contributes to social health (Brandtzaeg et al. 2022, Chaturvedi et al. 2023, Guingrich & Graziano 2024). Virtual agents capable of reciprocal adaptive behaviour are being tested for use in cognitive behaviour therapy and social skills training (Woo et al. 2024). 8 1 Translation competence in the age of generative AI 2.2 Modelling competence The reciprocity and mutuality of GenAI already go some way towards realising aspects of human-centred augmented translation so engagingly discussed by O’Brien (2024). She argues for HCAI as a framework for amplifying translators’ abilities and empowerment while maintaining human control. We would claim that GenAI represents a collaborative technology capable of complementing human agency and supporting empowerment to a degree unseen in dedicated (augmented) translation technologies to date. Even before ChatGPT launched the GenAI era late in 2022, Fügener et al. (2021: 1552) could confidently assert that “human performance can improve individually by receiving AI advice […] due to complementary knowledge”. Although their results also showed “significant downsides” when AI advice is provided as a “one-size-fits-all” solution in group decision-making, the individualisation made possible by appropriate prompting would suggest that this is no longer an issue. As in all complementary partnerships, the actants (to use the ANT term) must bring their own strengths to the table. Competent translators will for their part still need the transferable human skills that enable them to regulate cognitive and affective performance without AI support, the experience and knowledge on which they are predicated, and the competences necessary to deploy them within the sociotechnical environments where they work. These will have to be aligned with the new key skill of interacting with GenAI by prompting and reprompting LLMs,5based on knowledge and experience of how GenAI works. We further propose that this skill could be embedded in a broader area of HMI/ HAI competence (see below) which takes due account of GenAI’s technological agency.6 It is here that current TC models reveal inadequacies. Although they give due weight to the transferable personal and interpersonal skills that facilitate human agency, they have perpetuated an instrumentalist teaching agenda by focussing almost exclusively on aspects of CAT tool use (including MT) that not only underplay the human factor in HMI/HAI but also assume no technological agency. For example, in the case of the EMT competence frameworks, the only hint of possible reciprocal effects between the technological environment and human performance has been the inclusion of an organisational and physical ergonomic descriptor as an aspect of personal competence (EMT 2022: 10). 5See Chapter 5 by Yamada in the present volume. 6This is not to say that there is equivalence between conscious human agency and AI agency. However, they share features that suggest “family resemblances” in the sense developed by Wittgenstein (e.g. 1958: 32). 9 Gary Massey & Maureen Ehrensberger-Dow Tellingly, it makes no mention of ergonomic factors of technology design and use that play into cognitive performance, despite empirical evidence that they can be major enhancers, distractors or stressors (Ehrensberger-Dow et al. 2016). Otherwise, the EMT technology competence category focuses on non-interactive aspects of technology use: critically assessing, adapting to and effectively deploying available resources, including appropriate use of MT, file management and TMS, and demonstrating data literacy. Interactivity is visible only in interpersonal relations: checking, reviewing, revising and evaluating the work of others, working in multicultural and multilingual teams, interacting with clients and networking with language professionals and LSPs (EMT 2022: 8–11). The older but no less influential PACTE Group’s competence model (Hurtado Albir 2017: 35–41) is similar in this respect. It includes technology under what is called instrumental sub-competence, “related to the use of documentation resources and information and communication technologies applied to translation” (Hurtado Albir 2017: 40). The descriptor again demonstrates how competence models have fuelled the instrumentalist agenda criticised above.7 Nevertheless, the PACTE model has been a major source for others, who have supplemented it with additional components where appropriate. Those additions reflect a growing awareness that translation is far less a discrete cognitive act performed solely by the mind of an individual than an event of embodied cognition performed by complex systems involving humans, their social, technical and physical environments together with their cultural artefacts (Risku 2010: 103). For example, an interpersonal component is added to the most recent PACTEinformed model: Prieto Ramos’s (2024) adaptation of his previous model of legal TC (Prieto Ramos 2011) to institutional translation. The model’s “instrumental competence” category goes beyond the PACTE model by specifying the use of reliable resources for information mining, of computer tools for translation and revision tasks, and of MT (Prieto Ramos 2024: 155). But it does not speak to possible implications of human interactivity with agentic AI systems. Despite some overt (and acknowledged) similarities, Prieto Ramos (2024: 153) is highly critical of the latest EMT framework (2022) for failing to redress what he sees as a downgrading of thematic competence (i.e. domain specialisation, e.g. in law) in its previous iteration (EMT 2017). It is hard to argue against him, since thematic competence figures high on the list of professional expectations uncovered by his own research and that of others, especially with respect to 7The relevant descriptors in the highest level of the more recent competence framework (EFFORT) are similar, in that their focus is on non-interactive aspects of technology. https:// www.effortproject.eu/wp-content/uploads/Level-C-en.pdf 10 1 Translation competence in the age of generative AI legal and institutional legislative translation (Esfandiari et al. 2019, Lafeber 2023, Prieto Ramos & Guzmán 2023). The research receives equal credence from emic (insider) language industry heuristics. Describing the Slator (2022) “expert in the loop” model, for example, industry commentator Florian Faes responds to a question by the first author that only language professionals with high levels of domain and language expertise, combined with appropriate technological and prompt design/engineering competence, will provide the added human value needed to work with (Gen)AI and that such experts will stay in high demand (Faes & Massey 2024: 27–29). In other respects, however, the EMT framework (2022: 10) falls in with the broad consensus, already articulated above, that transferable personal and interpersonal skills (called generic or soft skills) are central to employability and to the adaptability needed in today’s work environment. The point is echoed by a recent PE model (Nitzke & Hansen-Schirra 2021: 69–79), visualised as a house, where the basic bilingual, extra-linguistic and information research competences that professional translators possess form the foundations. The roof is made of soft skills such as risk assessment, service competences, self-efficacy, a professional self-concept, an ethical attitude, concentration, stress-resistance, logical reasoning, analytical thinking and an affinity with technology. The model is one of several developed for tasks such as revision and PE (Robert et al. 2023), which were once considered integral aspects of TC. These reflect the growing diversification of language-industry tasks and the concomitant need for language professionals to have transferable skills enabling them to adapt to new activities. 2.3 Developing competence Industry diversification has prompted Angelone (2023) to call for fostering adaptive (as opposed to routinised) expertise across translator training curricula by consistently exposing learners to challenging situations where they need to apply their knowledge flexibly – itself a transferable skill set. But what balance should be struck between routine and adaptivity, between core and transferable skills? These are two dilemmas confronting translation teachers and their institutions with limited material and temporal resources at their disposal. Related to them is a third, namely employability tensions between domain or task specialisation (Prieto Ramos 2024) and a generalist approach more congruent with adaptivity training (EMT 2022). In all these cases, choices have to be made in specific educational contexts as to the most likely prospects for graduate employment, with curriculum developers nimble enough to adapt quickly to market changes. 11 Gary Massey & Maureen Ehrensberger-Dow title. Even when translators are explicitly being recruited, though, a broad range of skills seems to be expected. In their analysis of job notices from 2005–2020, Prieto Ramos & Guzmán (2023: 53–55) found that the duties of translators at supranational and intergovernmental organisations also included (in order of overall average mention): assistance with other tasks, terminology work, revision, CAT-tool management and editing. On the topic of how automation is changing the translation profession, Pym & Torres-Simón (2021) discuss various recommendations for translators to focus on what machines cannot (yet) do, such as language service advice, service provision, language consulting and high-stakes communication. This is consistent with comments from industry observers that text production with GenAI is less suitable for “content that has a regulatory or technical purpose” (Slator 2024: 75). The same report also predicts increased demand for multilingual experts to revise corporate-generated output to meet expected quality levels (Slator 2024: 63). LSPs already offer a wide range of AI-related services (Slator 2024: 101), many of which require linguistic expertise that may still be difficult to find (cf. Faes et al. 2024). The latest European language industry survey (ELIS 2025: 34) shows the only increase in technology implementation over 2024 to be in GenAI. This represents an opportunity for both training institutions and their graduates and, encouragingly, 64% of the students participating in the same survey reported using GenAI in their training, 19% regularly (ELIS 2025: 37). This raises one last important question about those who train translation students. The teachers themselves patently require the (Gen)AI literacy needed to understand AI techniques, critically assess AI productions and recommendations, and use AI creatively in their teaching (OECD 2023: 401).11 And institutions need to have the staff development procedures in place that empower their teachers to do so. 4 Final take-aways Whether employed as translators, linguists, transcreators, localisers, consultants or language professionals, our graduates have an important role to play in the language industry despite or because of the introduction of GenAI. But they will need appropriate skills to do so. And those skills will, above all, have to accommodate the dynamic interactions of human and AI agency. Though degrees of domain specialisation are open to debate and will need to be determined by local 11Ideas for course design, materials and teaching scenarios that align well with the skills and competences outlined here can be found in Pym & Hao (2025). 18 1 Translation competence in the age of generative AI conditions, current TC models already adequately cover the old-school translation and textual skills that industry demands. But their transferable and technological and digital skills are underspecified, and the introduction of GenAI calls for greater precision. Krüger’s (2024) AI literacy framework is a response, outlining the digital competences needed to harness AI efficiently, ethically and sustainably. It covers technical foundations (operating principles, training, etc.), assessing AI’s usefulness for (domain-)specific tasks, interacting with (Gen)AI, implementing AI in workflows, and understanding ethical and societal aspects. Partially overlapping with elements of Krüger’s framework, our own brief recommendations represent a set of concrete priorities for embracing GenAI in translator education. We recommend that collaborative experiential learning should always integrate decisions about whether and how to deploy GenAI, together with critical evaluations of the resultant processes and products. Outcomes should focus on developing specific transferable skills: • critical thinking • adaptivity • creative problem-solving • cognitive and emotional self-regulation • self-efficacy • accountability and a sense of ethics • collaborative ability in human and AI interactions. The extension of the last point aims to develop what we have termed HMI/ HAI competence, which includes: • designing effective prompts (translation purpose, client specifications, target genre, audience, etc.), evaluating GenAI responses with supplementary research (where appropriate) and reprompting • recognising priming effects and mitigating negative ones • identifying and eliminating bias, additions, omissions, hallucinations • understanding LLM data sources and knowledge time-lags. None of the above should be regarded as optional add-ons but must receive at least equal weight to so-called core skills. Only then will our graduates be properly equipped for the GenAI age, whatever roles they take on. 19 Gary Massey & Maureen Ehrensberger-Dow References Angelone, Erik. 2023. Weaving adaptive expertise into translator training. In Gary Massey, Elsa Huertas-Barros & David Katan (eds.), The human translator in the 2020s, 60–73. London, UK: Routledge. 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