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Purpose: This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice. Design/methodology/approach: A virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively. Findings: The GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain. Research limitations/implications: If repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation. Practical implications: While grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks. Social implications: As texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students. Originality/value: The novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.

This corpus-based study explores the expression of gratitude in the acknowledgement section of doctoral dissertations in both English and Arabic. The objective is to analyse how gratitude in academic discourse is structured in these languages and to explore any differences related to gender. The study examines 80 dissertations (40 in English and 40 in Arabic), further divided by the authors' gender (20 male and 20 female for each language). Data collection involved searching for doctoral dissertations from various sources, verifying the authenticity of the native language through biographical checks, and categorising the acknowledgements based on the year and field of study. The analysis utilised a mixed-methods approach based on Hyland and Tse's (2004) framework, enabling quantitative and qualitative text analysis. The study revealed distinct differences in gratitude expression patterns between English and Arabic dissertations: English acknowledgements often used performative verbs and adjectives, while Arabic acknowledgements primarily employed nominalisation. Notably, Arabic texts showed no use of passive constructions or adjectives. Gender-related differences were also observed, such as the preference for nominalisation among male Arabic authors and the preference for bare mention among female English authors. These findings underscore the significant cultural and linguistic influences on the construction of gratitude in academic acknowledgements. The research contributes valuable insights into pedagogical approaches to teaching academic writing and highlights the interplay between language, culture, and gender in scholarly communications.

The emergence of ChatGPT, a Generative AI program, has sparked discussions about its teaching and learning value, and concerns about academic integrity in higher education (HE). An extant review of the literature indicates that a scarcity of research exists on GenAI, specifically a synthesis of the official views, guidelines and articles of top-ranked universities on the use, limitations, challenges, and opportunities brought by ChatGPT in the early phase when ChatGPT was released in 2022 until early May 2023, which can offer insights into the concerns and recommendations for educators. Using the corpus assisted discourse analysis approach, this study identified the key themes and emotions elicited by evaluations of the ChatGPT situation from a self-built corpus containing 151 articles from 47 top-ranked U.S. universities and 34 U.K. ones. Our findings indicated three prominent themes discussed on official websites, including ChatGPT as a text and content generator, use of ChatGPT in teaching and learning, and potential implications and opportunities of using ChatGPT in HE. Further examination revealed that bias, concern, worry, threat, fear, and trust were the prevailing emotions relating to ChatGPT. Illustrated with examples collected from our corpus, this paper offers an in-depth discussion of universities' first responses to the use of ChatGPT. The insights gained have some pedagogical implications for academics, researchers and educators and may inform policy and practice in HE on the use of GenAI.

This research explores the experiences of Beth, a university student in the UK, as she comes to be labelled as 'dyslexic', and as she has her diagnosis taken away. Through use of Interpretative Phenomenological Analysis (IPA) and discourse analysis, the research seeks to understand how Beth made sense of these experiences, and to explore the discursive 'life' of dyslexia within this sense-making. The discussion in this paper proceeds chronologically through Beth's story, from 'struggle', to 'legitimation' to 'derogation', and concludes with a call to recognise the role of diagnosis in the field of special educational needs (SEN) from a social constructionist and relational perspective.