Teaching translation in higher education has undeniably been impacted by the innovations brought about by machine translation (MT), more particularly neural machine translation (NMT). This influence has become significantly more noticeable in recent years, as NMT technology progresses hand in hand with artificial intelligence. A case study supported by a questionnaire conducted among translation students (bachelor’s and master’s programmes at ISCAP) probed the degree of student satisfaction with CAT tools and revealed that they favour the use of MT in their translation practices, focusing their work on post-editing tasks rather than exploring other translation strategies and complementary resources. Although MT cannot be disregarded in translation programmes, as machine-generated translations make up an increasingly larger amount of a professional translator’s output, the widespread use of MT by students poses new challenges to translators’ training, since it becomes more difficult to assess students’ level of proficiency. Translation teachers must not only adapt their classroom strategies to accommodate these current translation strategies (NMT) but also, as intended by this study, find new, adequate methods of training and assessing students that go beyond regular translation assignments while still ensuring that students acquire the proper translation competence. Thus, as the use of NMT makes it considerably more challenging to assess a student’s level of translation competence, it is necessary to introduce other activities that not only allow students to acquire and develop their translation competence as defined in the EMT (European Masters in Translation) framework but also enable teachers to assess students more objectively. Hence, this article foregrounds a set of activities usually regarded as “indirect tasks” for technical translation courses that hopefully results in the development of student translation skills and competence, as well as provides more insights for teachers on how to more objectively assess students. It is possible, then, to conclude that these activities, such as different types of paraphrasing and error-detection tasks, may have the potential to encourage creative thinking and problem-solving strategies, giving teachers more resources to assess students’ level of translation competence.

As generative artificial intelligence tools like ChatGPT become increasingly integrated into educational environments, understanding their impact on critical thinking is crucial. Despite growing concerns about AI's potential to diminish students' independent reasoning, there is a lack of research tools specifically designed to evaluate students' perceptions of AI's cognitive capabilities. To address this gap, this study introduces the Perceived Critical Thinking Disposition of Generative Artificial Intelligence (PCTD-GAI) scale, aimed at measuring how students perceive generative AI’s (GAI) six critical thinking dispositions (reasoning, access to justice, search for evidence, search for truth, open-mindedness, and systematicity). While this study validates the scale using ChatGPT, the instrument is adaptable for evaluating other generative AI tools, supporting broader research in AI-driven learning environments, to assess not only how students engage with AI, but also how their reliance on AI may affect their cognitive development and self-regulated learning skills in digital education. To develop and validate the PCTD-GAI scale, the Marmara Critical Thinking Dispositions Scale (MCTDS) was adapted, ensuring relevance to AI assessment while maintaining conceptual robustness. A quantitative cross-sectional study was conducted with 931 university students from Portugal and Poland, employing exploratory and confirmatory factor analyses (EFA & CFA) to assess the scale’s validity and reliability. The results demonstrate that the PCTD-GAI effectively captures students’ perceptions of ChatGPT’s critical thinking dispositions across six key dimensions. Findings indicate moderately positive perceptions across both countries, with Portuguese students consistently rating ChatGPT marginally higher across domains and showing less response variability, suggesting greater consensus. Notably, perceptions were most neutral in the "truth-seeking" domain, while systematicity received the highest ratings, reflecting ChatGPT’s perceived systematic capabilities among students. These findings have significant implications for e-learning and AI-driven education. The PCTD-GAI scale enables educators to track students’ evolving AI literacy and develop targeted interventions that promote critical AI engagement rather than passive reliance on AI-generated content. Moreover, this research advances the field of e-learning by offering an empirical basis for integrating AI assessment into digital learning strategies, ensuring that AI serves as a cognitive tool rather than a substitute for independent reasoning. The validated PCTD-GAI scale provides a reliable, scalable method for assessing students’ perceptions of AI's cognitive capabilities, supporting evidence-based AI pedagogy, and guiding institutional policies on AI integration in education.

According to a recent study by OpenAI, Open Research, and the University of Pennsylvania, large language models (LLMs) based on artificial intelligence (AI), such as generative pretrained transformers (GPTs), may have potential implications for the job market, specifically regarding occupations that demand writing or programming skills. This research points out that interpreters and translators are one of the main occupations with greater exposure to AI in the US job market (76.5%), in a trend that is expected to affect other regions of the globe. This article, following a mixed-methods survey-based research approach, provides insights into the awareness and knowledge about AI among Portuguese language service providers (LSPs), specifically regarding neural machine translation (NMT) and large language models (LLM), their actual use and usefulness, as well as their potential influence on work performance and the labour market. The results show that most professionals are unable to identify whether AI and/or automation technologies support the tools that are most used in the profession. The usefulness of AI is essentially low to moderate and the professionals who are less familiar with it and less knowledgeable also demonstrate a lack of trust in it. Two thirds of the sample estimate negative or very negative effects of AI in their profession, expressing the devaluation and replacement of experts, the reduction of income, and the reconfiguration of the career of translator to mere post-editors as major concerns.

The societal disturbance created by the rapid outbreak of the COVID-19 pandemic has shaken the entire globe, profoundly affecting all levels of education. The challenge presented by COVID-19 is broad, rapidly evolving, and complex; it threatens everyone’s well-being, the global economy, the environment, and all societal and cultural standards and our daily activities. Throughout the Coronavirus outbreak and any future lockdowns, it is crucial that the needs of students be ultimately and regularly met and that they are supported effectively. We intend to address skill shortages and mismatches, particularly regarding the readiness to teach in an online environment that encourages flexible and innovative learning. The main contribution of this paper is addressing this subject with an integrated vision of three different players in higher education: students, teachers and librarians. Using the Technology Adoption Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), a conceptual model was developed to explain both the behavior and intentions of users when using e-learning systems. Among Portuguese students, 91% of e-learning satisfaction can be attributed to perceived usefulness, actual use, and personal considerations. For educators, satisfaction appears to be mostly dependent on perceived usefulness and usability, while librarians’ satisfaction is negatively dependent on technological factors. Students’ actual use of technology is 89% dependent on organizational and technological variables. However, the actual use by teachers appears to be primarily dependent on personal and technological factors. Similarly, 91% of the variability of the use of e-learning tools by librarians can be explained by organizational, personal and technological factors, with the personal factors having a negative impact on the actual use.

Recent advances in Artificial Intelligence (AI), especially large language models (LLMs), are rapidly transforming educational practices worldwide. Despite growing adoption, there is limited cross-national evidence on how university students perceive and engage with these technologies. This study addresses this gap by analyzing data from 1906 students across 13 countries, using a novel psychometric approach that leverages LLMs for item generation and validation. Our findings reveal that students’ trust in AI, familiarity with algorithms, and lower anxiety about AI are key predictors of positive perceptions and adoption of LLMs in learning. Notably, most students with even minimal trust in AI reported using LLMs, primarily for coding, idea generation, and writing, and perceived them as at least moderately effective. In contrast, students who did not use LLMs were unlikely to use other AI tools. These results underscore the importance of fostering trust and AI literacy to support effective integration of AI technologies in tertiary education. Practical implications include the need for targeted training programs, transparent data practices, and strategies to address student anxiety and promote responsible use. By providing cross-national insights and introducing innovative measurement tools, this study offers actionable recommendations for educators, institutions, and policymakers seeking to enhance student engagement with AI in diverse educational contexts.