Translation technologies have historically been developed without substantial input from professionals (e.g. O’Brien 2012). Conversely, the emerging human-centered AI (HCAI) paradigm emphasizes the importance of including end-users in the “process of conceiving, designing, testing, deploying, and iterating” technologies (Vallor 2024: 17). Therefore, early research engagement on the attitudes, needs and opinions of professionals on AI implementation is essential because incorporating them at later stages “results in issues and missed opportunities, which may be expensive to recover from due to the cost, time, resources, and energy spent” (Winslow and Garibay 2004: 123). To this end, this article presents a qualitative analysis of professional translators’ attitudes towards AI in the future, centered around the role of MT and post-editing (PE). The discussion draws on data collected from open ended questions included in a larger survey on control and autonomy from a HCAI perspective, which were thematically coded and qualitatively examined. The thematic analysis indicates that predominant concerns regarding the future of the AI-driven translation industry still revolves around longstanding issues in PE and MT literature, such as PE, translation quality, communicating and educating LSP, clients, users, and the broader public, maintaining human control over the final product or creativity. This is explained to some extent to the relatively small rates of integration of AI technologies into translation workflows to date (e.g. ELIA 2024; Rivas Ginel et al 2024; GALA 2024; Jimenez-Crespo 2024), or the fact the professional report using AI primarily for tasks related to translation, but not necessarily to PE the output of LLMs or NMT (Rivas Ginel and Moorkens 2025).

During recent years, MT research has imported a number of conceptual tools from Translation Studies such as “translationese” or “translation universals”. These notions were the object of intense conceptual debates in Corpus-Based Translation Studies (CBTS), and number of seminal publications and conference forums recommended substituting them by less problematic terms such as “the language of translation” or “typical” or “general features of translated language”. This paper critically analyses the arguments put forward in the early 2000’s in CBTS to against the use of these terms, and whether the same issues apply to current MT re-search using them. Here, the paper will discuss, (1) the impact of the negative or pejorative nature of the term “translationese” on the status of professional translators and translation products in academia and society (2) the danger of over-generalizations or overextending claims found in specific and very limited textual subsets, as well as (3) the need to reframe the search of tendencies in translated language away from “universals” towards probabilistic, situational or conditional tendencies. It will be argued that MT re-search would benefit from clearly defined terms to deal with notions related to language variation in specific new variants of translation, proposing neutral terms such as “NMT translated language” or “the language of NMT”, as well as “general features/ tendencies in NMT / PE translations”. A proposal will be made in order to reach a “convergence” of MT and TS research and the probabilistic and descriptive study of features of (NMT or human) translated language.

The Covid pandemic upended translation teaching globally. The forced move to online teaching represented a gargantuan challenge for anyone only experienced in face-to-face teaching. Online translation teaching requires distinct approaches to guarantee that students can reach the targeted learning goals. This paper presents a literature review on the provision of effective feedback in the light of these drastic changes in translation teaching as well as a description as how existing research on online feedback for translation training has been applied to the design of online courses at the translation program at Rutgers University.