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Ethics in translation is often treated as a marginal or theoretical concern, rather than a central, integrated component of the curriculum. This mixed-methods study aims to investigate students’ perceptions of ethical principles in

Ethics in translation is often treated as a marginal or theoretical concern, rather than a central, integrated component of the curriculum. This mixed-methods study aims to investigate students’ perceptions of ethical principles in translation teaching in the era of AI-supported education. 416 fourth-year students from a university in Vietnam completed a structured questionnaire. Eleven students were randomly selected for semi-structured interviews to explore deeper insights into their ethical perspectives. While students showed ethical awareness of copyright and confidentiality, they lacked sufficient understanding of or interest in privacy policies. They highly appreciated professional values, acknowledging the role of human translation in refining AI-generated translations. Still, they paid little attention to sustainability and justice, which are implicit aspects of AI use in translation. The findings highlight the need for curriculum integration of translation ethics in the AI-enabled era to train both competent and ethically grounded novices who can evaluate platform terms, form privacy-aware decision-making habits, and engage with sustainability and justice. Clearer AI platform policies are needed to support responsible and ethical use of translation technologies. Future research should explore students’ ethical decision-making in AI-based translation and the impact of AI tools on students’ professional development and translators’ agency.

New generative AI (GenAI) tools are largely a product of the Global North but have rapidly spread worldwide. This scoping review examines the current perceptions of Generative AI in higher education across the Global South by analysing 75

New generative AI (GenAI) tools are largely a product of the Global North but have rapidly spread worldwide. This scoping review examines the current perceptions of Generative AI in higher education across the Global South by analysing 75 papers published between 2022-2025. Following the PRISMA-ScR methodology, the review categorised the findings into five main areas: GenAI acceptance and adoption, implications and challenges, academic integrity considerations, educational practices, and equity concerns. The analysis reveals that GenAI offers transformative possibilities for personalised learning, research support, and administrative efficiency in higher education across the Global South, but its implementation faces significant barriers, including infrastructure limitations, human capital deficiencies, ethical concerns, inadequate policy frameworks, and contextual challenges. Notably, equity considerations have received the least research attention despite their critical importance in inclusive education. This review also identified substantial gaps in the literature, including limited geographic representation, stakeholder imbalance, and insufficient exploration of long-term outcomes. The authors recommend that future research prioritise equity-centred approaches, methodological diversity, contextual specificity, implementation science, and interdisciplinary collaboration to better understand how Generative AI can enhance educational opportunities without reinforcing existing disparities across the diverse contexts of the Global South.

Artificial Intelligence (AI), particularly Generative AI (GenAI), is profoundly reshaping assessment practices in computer science education by enabling automation, scalability, and personalized feedback mechanisms. AI-enhanced tools

Artificial Intelligence (AI), particularly Generative AI (GenAI), is profoundly reshaping assessment practices in computer science education by enabling automation, scalability, and personalized feedback mechanisms. AI-enhanced tools facilitate adaptive testing, real-time learner support, and data-driven insights that foster deeper engagement and learning outcomes. However, the integration of AI also raises critical concerns related to academic integrity, algorithmic bias, transparency, and ethical implications of AI-driven evaluation. This theoretical paper critically examines the opportunities and complexities of GenAI-enabled assessment in CS education. Drawing on contemporary literature, pedagogical theory, and emerging use cases, the paper explores how GenAI can enhance student engagement, support equitable learning, and relieve instructional burden—while also highlighting the need for ethical safeguards and pedagogically grounded frameworks. It argues that the successful integration of GenAI depends not solely on technological capability but on deliberate, human-guided design that upholds transparency, fairness, and educational purpose. By advancing a nuanced and critically reflective perspective, this paper contributes to the evolving discourse on AI in education and provides actionable insights for educators, researchers, and policymakers seeking to harness GenAI responsibly in the future of computer science education.

Generative artificial intelligence (GenAI) presents journalism educators with a paradox. It promises efficiencies and new creative possibilities for students, yet threatens academic integrity, authorship, and the core values of the

Generative artificial intelligence (GenAI) presents journalism educators with a paradox. It promises efficiencies and new creative possibilities for students, yet threatens academic integrity, authorship, and the core values of the profession. This paper presents a practice-based curriculum intervention based on an ethical framework that embeds GenAI into undergraduate journalism assessment. The framework integrates Colin Beard’s Experiential Learning Design with Bloom’s revised taxonomy to scaffold students’ movement from human-only reporting tasks to AI-integrated work and critical reflection. Qualitative data from a second-year journalism unit at an Australian global campus in Sarawak, Malaysia, reveal that students already use GenAI strategically but hold deep concerns about accuracy, originality and disclosure. These findings inform the design of authentic, scaffolded activities that aim to convert students’ intuitive concerns into explicit competencies in ethical decision-making, prompt engineering and verification. The paper contributes a model that links Australian and institutional quality expectations to the realities of GenAI in journalism education in Malaysia campus.

As generative AI (GenAI) tools increasingly influence higher education, instructors face urgent decisions about how to address these technologies in their teaching, often without institutional policies or clear guidance. This study

As generative AI (GenAI) tools increasingly influence higher education, instructors face urgent decisions about how to address these technologies in their teaching, often without institutional policies or clear guidance. This study examines how academic development can support educators in making ethical, pedagogically sound decisions about GenAI integration. Grounded in a practitioner inquiry framework, we analyse data from a workshop, Communicating about Ethical AI Use with Learners. Participants engaged in structured reflection, peer discussion, and syllabus policy drafting activities designed to surface values, clarify learning goals, and build confidence in communicating course-level GenAI expectations. Findings from participant-surveys (pre- and posttests) and workshop artifacts reveal that faculty overwhelmingly emphasize academic integrity in discussions of course AI policies, but also express concerns about equity, access, and student learning. Participants reported increased confidence in their ability to create and communicate GenAI policies after engaging in the workshop. We argue that academic development functions as ethical infrastructure, offering not only policy templates but also reflective space to support human-centred and context-sensitive decision-making. This article contributes a practice-informed model of GenAI ethics that can guide faculty developers and instructors in designing responsive learning environments. It offers insights for those seeking to foster ethical awareness, transparency, and pedagogical intentionality in a rapidly evolving technological landscape.

This article explores the integration of artificial intelligence (AI) in an English course for Academic and Professional Communication, situated within the broader context of digital transformation in higher education and the evolving

This article explores the integration of artificial intelligence (AI) in an English course for Academic and Professional Communication, situated within the broader context of digital transformation in higher education and the evolving demands of academic integrity. Rather than restricting AI use, the study adopts a critical digital pedagogy approach to foster students' awareness of ethical and effective engagement with AI tools. The activities were designed to encourage reflective practices, promote critical analysis of AI-generated content, and establish clear guidelines for responsible use. These activities include setting explicit expectations for AI-assisted work, integrating reflective assessments to evaluate AI outputs, and designing tasks that strengthen students’ critical thinking and academic integrity. Additionally, the approach aims to improve students’ retention of the course content by encouraging them to use AI not as a shortcut, but as a means to actively reflect, process, and engage with the material explained in class. By interacting with AI to rephrase, question, edit, or elaborate on key concepts, students become more conscious of the content, reinforcing understanding and promoting deeper learning. The results suggest that the students developed a more nuanced understanding of the capabilities and limitations, enabling them to engage with it as a support tool rather than a substitute for the original work. The study concludes that structured integration of AI can enhance students' digital literacy, content retention, and critical thinking skills in the academic context.

Raising students’ ethical awareness of generative artificial intelligence (GenAI) is critical as higher education responds to rapid technological change. This study investigates how guided reflection embedded within self‑assessment can

Raising students’ ethical awareness of generative artificial intelligence (GenAI) is critical as higher education responds to rapid technological change. This study investigates how guided reflection embedded within self‑assessment can support students to engage responsibly and confidently with GenAI in learning and assessment, while maintaining academic integrity. The self‑assessment, designed to foster readiness and learner agency, prompted students to reflect on their use of GenAI for critical thinking, writing, and understanding disciplinary concepts, positioning them as reflective practitioners capable of navigating ethical dimensions of AI use. Data were collected through pre‑ and post‑semester surveys administered to commencing university students. Likert‑scale items measured confidence in academic and digital literacies, while an open‑text response invited reflection on the ethical implications of GenAI. Qualitative data were thematically analysed to examine ethical engagement and emerging learner agency. Findings indicate that students recognised both the benefits and limitations of GenAI, demonstrating ethical awareness and critical evaluation of its role in academic work. Reflections revealed tensions between efficiency and academic integrity, as well as uncertainty about institutional expectations. Students also reported an increased sense of personal responsibility, describing the reflective process as confidence‑building during their transition to university study. The evolving role of educators involves supporting students to engage critically and responsibly with emerging technologies. Embedding GenAI‑focused reflection within self‑assessment reframes ethical inquiry as a pedagogical strategy. This study contributes to scholarship on ethical GenAI use and offers a scalable, student‑centred approach that can be adapted across disciplines and year levels.

Since generative artificial intelligence (genAI) applications became widely available in November 2022, university students and teachers have confronted choices about using genAI in coursework, research, and other tasks. In this article

Since generative artificial intelligence (genAI) applications became widely available in November 2022, university students and teachers have confronted choices about using genAI in coursework, research, and other tasks. In this article, we investigate Swedish university students’ and teachers’ perspectives on the ethics of genAI (non)use in higher education. Data comes from anonymous responses to voluntary online surveys and five focus group interviews with undergraduate and postgraduate students and teachers in the social science faculty of a Swedish public university. We ask what ethical concerns informed research participants’ perceptions and (non)use of genAI, and draw on the discipline of moral philosophy, particularly the work of environmental ethicist Carolyn Merchant, to consider how higher education institutions might work holistically with this material to support ethical genAI (non)use. Merchant proposes “partnership ethics” as an environmentally-informed, dialogue-based alternative to the ethics that often dominate decision-making. We argue that partnership ethics can guide university teachers and students in making decisions about this rapidly evolving technology and offer practical suggestions for implementing partnership ethics in higher education settings.

60% of higher education institutions worldwide reported that their faculty used generative artificial intelligence tools in teaching. However, fewer than 15% had implemented formal ethical guidelines. This issue underscores the importance

60% of higher education institutions worldwide reported that their faculty used generative artificial intelligence tools in teaching. However, fewer than 15% had implemented formal ethical guidelines. This issue underscores the importance of equipping English for Academic Purposes educators in higher education to utilise artificial intelligence effectively while maintaining academic integrity, transparency, and equity. This study addressed the lack of evidence from Global South contexts, the shortage of field-tested professional development models, and the limited integration of technical and ethical training. Guided by Mezirow's Transformative Learning Theory and established ethical decision-making frameworks in educational technology, the study designed, implemented, and evaluated a GenAI-integrated Professional Development program that embeds ethical reasoning within a multicultural Global South institution. A convergent mixed-methods approach engaged 97 higher education educators in quantitative testing and 15 in qualitative interviews. Original instruments, including a Scenario Rubric, Artefact Rubric, and engagement index, measured pre- and post-program changes. Quantitative results showed significant gains in ethical awareness (d = 0.93) and digital andragogical competence, supported by high inter-rater reliability. Qualitative findings confirmed challenges such as plagiarism and student overreliance on artificial intelligence, and revealed an unexpected barrier: reconciling institutional policy gaps with personal ethical values. Recommendations include embedding artificial intelligence policy literacy into professional development, fostering peer-led communities of practice, and creating shared resources on artificial intelligence. The professional development model can be adapted for use in other contexts, and future research should investigate its scalability.

Generative artificial intelligence has become part of everyday academic work despite many questions centring around its ethical, pedagogical, and institutional aspects. This paper synthesises the results of two questionnaire studies from

Generative artificial intelligence has become part of everyday academic work despite many questions centring around its ethical, pedagogical, and institutional aspects. This paper synthesises the results of two questionnaire studies from the European higher educational context, one administered with students (n = 226) and another with instructors (n = 256). Parallel five-point scales measured AI literacy, motivation, ethics, intention, and support. Independent-samples t-tests revealed significant group differences: students rated their ethical awareness higher than instructors, while instructors reported stronger willingness to experiment with AI tools and reported higher behavioural intention, which might be because students tend to view ethics through their immediate coursework practices, whereas teachers interpret it through the lack of institutional clarity and treat is as a question of integrity. Cluster analysis of instructors identified three user groups that differed in several literacy dimensions, with the scale of ethics providing a clear distinction. Correlation analyses indicated that instructors’ moral awareness and intention grow parallel with institutional and social support, while students’ confidence and readiness correlate mostly with self-efficacy and collaboration rather than with formal instruction.

Generative Artificial Intelligence (GenAI) has rapidly emerged as a dramatic disruptor across multiple sectors, significantly impacting higher education and workplace dynamics (Baidoo-anu & Owusu Ansah, 2023). Understanding how students

Generative Artificial Intelligence (GenAI) has rapidly emerged as a dramatic disruptor across multiple sectors, significantly impacting higher education and workplace dynamics (Baidoo-anu & Owusu Ansah, 2023). Understanding how students and educators engage with the complexities, challenges, and affordances of GenAI is essential to developing robust institutional educational responses. In 2023, we administered a survey to academic staff at our Australian-based University to explore their perspectives, attitudes and experiences regarding GenAI in learning and teaching. Findings revealed substantial concerns regarding academic integrity, ethical considerations, and the potential erosion of essential academic skills such as critical thinking and creativity. Staff highlighted equity concerns related to student access and proficiency in GenAI tools and digital literacy, with uncertainty about staff responsibilities in training students. Conversely, staff recognised substantial opportunities for using GenAI to enhance teaching practices through streamlined administrative processes, improved curriculum and assessment design, and innovative, student-centred learning activities. Given our findings, there is an urgent need for clear governance frameworks, structured professional training programs, interdisciplinary collaboration, and the active inclusion of student perspectives.

This study examines the organisational and technological determinants influencing the adoption of artificial intelligence (AI) technologies specifically data-driven decision support systems (DSS) and smart learning platforms in Saudi

This study examines the organisational and technological determinants influencing the adoption of artificial intelligence (AI) technologies specifically data-driven decision support systems (DSS) and smart learning platforms in Saudi Arabian higher education institutions. Guided by the integrated Technology–Organisation–Environment (TOE) framework and Diffusion of Innovations (DOI) theory, the study employs a quantitative approach using a structured survey administered to 300 academic and administrative staff across multiple institutions. The survey captures key constructs, including social trends, organisational culture, sustainability practices, waste management, technology compatibility, relative advantage, complexity, social drivers, innovation attributes, intention to use data-driven DSS, and government regulatory support. Structural equation modelling (SEM) was applied to test the hypothesised relationships and evaluate the interplay among organisational, technological, and environmental factors. The results reveal that internal organisational conditions, particularly organisational culture, sustainability policies, and waste-management practices- significantly strengthen internal social drivers that promote technological change. Technological characteristics, including Compatibility with existing systems and perceived relative advantage, strongly enhance the perceived innovation attributes of AI technologies. These innovation attributes, in turn, serve as a major catalyst for both the intention to use and actual adoption of AI-enabled systems. Adoption within the higher education context was associated with improvements in teaching practices, enhanced learner engagement, and curriculum innovation. Moreover, government regulations and policy incentives emerged as crucial external enablers that facilitate institutional readiness and adoption. This study contributes to the growing body of knowledge on AI integration in higher education by providing an empirically validated model that highlights the interaction between organisational drivers, innovation characteristics, and environmental enablers. The findings offer practical insights for policymakers, higher education leaders, and system designers seeking to accelerate responsible and effective AI adoption in academic environments.

This paper explored students' use of Generative Artificial Intelligence (GenAI) for Objective Structured Clinical Examinations (OSCE) preparation using a retrospective cohort study conducted over two years (2023–2024) across Australia and

This paper explored students' use of Generative Artificial Intelligence (GenAI) for Objective Structured Clinical Examinations (OSCE) preparation using a retrospective cohort study conducted over two years (2023–2024) across Australia and Malaysia. Results analysed OSCE grades and written self-reflections from students. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was applied as a lens to interpret qualitative data using summative content analysis. Of 997 students, 163 (16.3%) stated they did use GenAI to prepare for OSCEs. Across both campuses, there was no significant difference in mean OSCE grades between GenAI users (79.9%, SD = 15.3) and non-GenAI users (79.6%, SD = 17.4; p = .6); however, non-GenAI users performed significantly better (p < .05) in four of the seven communication rubric criteria. Themes around mistrust or perceived inaccuracy of GenAI data for clinical application deterred use in non-users. From our results, the use of GenAI did not demonstrate additional benefits for overall OSCE preparation, suggesting the potential need for more pedagogically aligned applications of GenAI tools to maximise their utility for clinical assessment preparation.