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.

Sustainability has become one of the main drivers of organizational performance. This study investigates the integration of sustainability with the balanced scorecard (BSC) as a framework for translating environmental management strategy into organizational performance. This review also seeks to map sustainability balanced scorecard (SBSC) research, clarifying its current role and identifying gaps and opportunities for future research. To achieve this, we sourced and reviewed 247 publications from the Web of Science index, corresponding to 129 scientific journals and 57 conference proceedings. Our analysis included content analysis and bibliometric analysis performed using the R packages Bibliometrix (version: 4.3.5), Biblioshiny, and CiteSpace (6.3.R1 Basic). The findings revealed that the SBSC enhances organizational capacity to align sustainability with strategic objectives, although significant implementation barriers remain, such as the selection of appropriate sustainability indicators and organizational resistance. This study contributes to advancing the theoretical and practical understanding of the SBSC while offering pathways for future research and application across sectors.

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.

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 on the job market, specifically regarding job occupations that demand writing or programming skills. The research points out Interpreters and Translators as one of the main job occupations with greater exposure to AI in the US job market (76.5%). This article, following a mixed-methods survey-based research, provides insights into the Portuguese Language Service Providers (LSPs) awareness and knowledge about AI, specifically regarding neural machine translation (NMT) and large language models (LLM), its actual use and usefulness, as well as their potential influence on work performance and labour market. The results show that most professionals are unable to identify if 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 lack of trust in it. Two thirds of the sample estimate negative or very negative effects of AI in the profession, expressing as main concerns the devaluation and replacement of experts, the reduction of income, and the reconfiguration of the career of translator to mere post-editors.