As the adoption of large language models (LLMs) continues to redefine translation as a professional practice and the skills required to effectively leverage assistive technologies, the academic community is responding by proposing approaches to integrating generative AI into translator training (see Penet, Moorkens and Yamada 2026; Pym and Hao 2025; Kornacki and Pietrzak 2024). Such approaches are ideally situated within a field-specific AI literacy framework, such as that recently introduced by Krüger (2025) in extending on previous modelling of MT and data literacy for translation. Within the interaction dimension of his AI literacy framework, Krüger outlines a cognitive level, which, among other things, encompasses the translator’s awareness of how AI use can both augment and impair human intelligence (2025:18). From a metacognitive perspective, indicators of interaction-oriented AI literacy include the translator’s ability to recognize and articulate the affordances and constraints of AI systems in relation to their own performance, and to act accordingly. This paper presents a preliminary, process-oriented approach built around think-aloud protocols and screen recording that translation trainers and trainees can draw on to identify potential instances of augmentation (new-skilling) and impairment (skill-skipping, no-skilling, de-skilling) (Weßels and Maibaum 2026) in conjunction with the utilization of generative AI. In doing so, it contributes to the growing body of literature on critical AI literacy and informed pedagogical implementation of generative AI. References Kornacki, M., & Pietrzak, P. (2024). Hybrid Workflows in Translation: Integrating GenAI into Translator Training (1st ed.). Routledge. https://doi.org/10.4324/9781003521822 Krüger, R. (2025). Implementing generative artificial intelligence technologies in language industry workflows – A competence perspective. Lebende Sprachen, 70(1), 11–38. https://doi.org/10.1515/les-2025-0016 Penet, JC, Moorkens, Joss & Yamada, Masaru (eds.). 2026. Teaching translation in the age of generative AI: New paradigm, new learning?. (Translation and Multilingual Natural Language Processing 25). Berlin: Language Science Press. DOI: 10.5281/zenodo.17580856 Pym, A., & Hao, Y. (2024). How to Augment Language Skills: Generative AI and Machine Translation in Language Learning and Translator Training (1st ed.). Routledge. https://doi.org/10.4324/9781032648033 Weßels, D. & Maibaum, M. (2026). Vom Deskilling zum Newskilling. Forschung & Lehre, 2(2), 30-32. https://esv-elibrary.de/journal/article/99.160005/ful.2026.02.11

Pauses are known to be good indicators of cognitive demand in monolingual language production and in translation. However, a previous effort by O’Brien (2006) to establish an analogous relationship in post-editing did not produce the expected result. In this case study, we introduce a metric for pause activity, the average pause ratio, which is sensitive to both the number and duration of pauses. We measured cognitive effort in a segment by counting the number of complete editing events. We found that the average pause ratio was higher for less cognitively demanding segments than for more cognitively demanding segments. Moreover, this effect became more pronounced as the minimum threshold for pause length was shortened.