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.

This paper aims to understand the effect of content and digital influencer characteristics on their persuasiveness ability and Generation Y's resulting brand attitudes and purchase intentions (Millennials). Based on a sample of 201 individuals and quantitative analysis, data was analysed using Smart PLS. The results show that the quality of the content is the characteristic with the highest impact on the influencer's persuasive power. In terms of influencer characteristics, trust is a critical determinant. The influencer's persuasiveness is essential for developing a positive attitude towards the brand and purchase intention. Our findings update previous studies, focusing on Generation Y and including trust as a variable while simultaneously revealing a fundamental determinant of influence power. For brands, this study confirms the importance of having digital influencers in their strategies. For influencers, it suggests that when targeting Generation Y, they should invest in the quality of their publications and increase trust, as this is strongly linked to their persuasiveness and, consequently, to the purchase intention of their followers

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.

The adoption of social media networks by organizations has been increasing, mainly by using more social networks but also by constantly increasing on the number of messages and received comments posted on these channels. Interestingly, this process apparently has not been accompanied by a carefully planned and strategically design process to provide the essential alignment with organizational goals. This study is framed in the tertiary sector, the Higher Education Sector (HES), which despite its peculiarities, is no exception to the above limitations, and is facing an increased competitive environment.In this paper we present a sector benchmarking process, and the respective analysis, to provide insights on the sector's tendency, as well as a threefold classification of the sector's social media strategies being pursued. The analysis builds upon a regulatory communication framework and respective editorial model. We describe the results of our automatic text-mining and categorization information system, specifically developed to address and analyze the seven categories of HES’ social media messages.Our results show that social media strategies have been focusing essentially on mediatization and building/maintaining the organizational image/reputation as well as on advertising educational services, but completely neglecting the dialogical dimension intrinsically linked to social media environments

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.