Introduction. Over the last two decades, research has suggested that academic and disciplinary literacies (ADLs) are key to integrating content and language, as language is used to express content knowledge, often through Cognitive Discourse Functions, discourse patterns that respond to cognitive actions in formal education contexts. Nevertheless, systematic assessment tools are required to assess student production of ADLs. Purpose. This validation study focuses on developing an analytic rubric to measure three ADL skills, consisting of three dimensions. Two of these dimensions are CDFs: students’ skills to argue and compare. A third rubric measures an additional academic skill, students’ ability to write from sources. The rubrics are designed to capture cross-disciplinary and multilingual productions of these skills, therefore being applicable for various disciplines (history, science, mathematics, among others…) and languages. Methods. A five-step validation process based on expert judgement was used, involving 13 international experts. They quantitatively and qualitatively evaluated the proposed rubrics based on pertinence, conceptual clarity, coherence, and relevance across two iterative rounds. Quantitative descriptive statistics and agreement indices were used, in addition to thematic analysis of qualitative feedback. Findings. The quality of the rubric showed clear progression between validation rounds. In the first version, several issues were raised by the experts–most criteria showed weak agreement and low means. After revisions, there was a substantial improvement in the second version, with 83 % of the criteria reaching strong or acceptable agreement. Qualitative feedback highlighted the need for precise and multidimensional operationalisations in each ADL dimension. To illustrate the application of the rubrics, multilingual student samples are provided. Conclusions. The rubrics offer the first steps towards systematising ADL assessment by combining qualitative and quantitative feedback from 13 experts, underscoring the importance of expert input in advancing assessment practices. The study has theoretical and practical contributions: it highlights the multidimensional nature of ADLs and provides an adaptable rubric that can be used across disciplines, languages and educational contexts. This study focused on the validation process; therefore, empirical use of the rubric is still required. Future research should apply the rubrics to large-scale corpora and complement the expert-based validation with psychometric approaches.

Introduction: Stylistic deviations such as wordiness and redundancy undermine clarity and precision in academic writing. Their frequency and communicative impact, however, are likely to vary across disciplinary traditions. Earlier research has examined these phenomena in education-related corpora and revealed patterned distributions of redundancy in justificatory passages. By contrast, the ways in which such deviations manifest in linguistics research articles remain underexplored. Results: Wordiness predominated across the corpus, accounting for 70.6% of all deviations, while redundancy accounted for 29.4%. Class balance was stable across Moves and Steps, but severity was functionally localized: high-impact deviations clustered in M3_S3, M3_S1, M2_S2, and M1_S3. Almost all high-impact cases were linked to wordiness, with syntactic complexity alone responsible for 54 of 61 instances. Redundancy, although frequent in structural and lexical repetition, rarely reached high severity. Conclusion: These findings show that in linguistics Introductions in English the primary stylistic risk lies not in repetition but in syntactic overload at rhetorically dense points of the text. The results extend previous applications of the taxonomy by demonstrating a discipline-specific pattern of risk concentration. The study highlights the value of combining rhetorical segmentation with fine-grained stylistic annotation and suggests that pedagogical efforts should focus on reducing syntactic complexity in high-pressure rhetorical contexts. Limitations include the modest corpus size and the absence of cross-disciplinary comparison, which future research should address to refine understanding of stylistic risk across fields.

Background: The field of language education is experiencing substantial advancements with the introduction of Generative Artificial Intelligence (GenAI). While there is a significant amount of research on teachers’ perceptions in language education, there is a noticeable gap in understanding the integration of ChatGPT into this field. Purpose: This study investigated the perceptions of EFL teachers in Iran regarding how ChatGPT can contribute to the enhancement of language teaching practices and pedagogical awareness. Method: A qualitative case study examined teachers’ perspectives on using ChatGPT in language education. A Zoom webinar on AI in teaching was conducted for Iranian EFL teachers, using tools like Focusky. From 73 registered teachers, 23 were selected based on ≥2 years’ teaching experience and voluntary participation. Data were gathered through focus groups, semi-structured interviews (n=10, in Persian), and reflective essays (n=10) submitted two weeks post-webinar. Inductive thematic analysis was performed using NVivo 12, with rigor ensured via peer debriefing, member checking, cross-case comparison, and 83% inter-coder agreement. Results: Data analysis revealed that ChatGPT can promote pedagogical awareness (i.e., enhanced professional development opportunities, enhancement of critical thinking skills, and autonomy and confidence) and pedagogical practices (i.e., understanding students’ needs and personalizing teaching and content creation and resource development). Conclusion: These findings suggest that ChatGPT can positively impact language teachers without necessarily sacrificing their jobs, which is the dominant concern in the current academic literature.

Background: For a long time, concepts such as the intercultural communicative competence have challenged the view of culture as purely fact-based, objective content to be transmitted to students, from whom only retention is expected. Today, cultural knowledge plays virtually no role in international standardised English exams such as TOEFL or the Cambridge English Qualifications. It is unknown to which extent this is true for examinations at the national level, and the materials used to prepare students for these examinations. Methods: The study provides a quantitative account of exam topics in the speaking part of the final exam in Czech grammar schools. The sample consists of 206 grammar schools in the Czech Republic (58% of all such schools). The percentage of knowledge-oriented, culture-specific topics was calculated for each school. The study further analyses the content of four books used to prepare students for the exam. The sample comprises four frequently used preparatory books. The text in the chapters that deal with cultural topics is classified using Larzén’s (2005) typology of culture. Results: The study has demonstrated that approximately 50% of the final exam topics are focused on specific English-speaking countries, their geography, history and literature. A similar tendency, but significantly more pronounced, has been found in the preparatory materials. More than 95% of their content was heavily knowledge-oriented with no discernible difference between older and recently published materials. Virtually no evidence of a skill-based or intercultural approach to culture was found. Conclusions: The study has confirmed what is often only an unfounded assumption by researchers and it has shown that the traditional factual view of culture is still immensely popular with no change in sight. Recommendations for material writers, teachers, and teacher educators are provided at the end of the study.

Background. The trend of promotional language (hypes) in academic discourse, such as critical, robust, new, discover, and undoubtedly, has raised concerns about the changing nature of scholarly communication. While previous studies have documented this trend in published texts, the motivations driving hype usage among developing academic writers, especially in EFL context, remain underexplored. Purpose. This paper investigates Indonesian EFL learners’ use of hypes in theses and dissertations, examines their perceptions and the factors that motivate hype usage in unpublished academic texts. Materials and Methods. Through purposive sampling, we conducted in-depth semi-structured interviews with 12 Indonesian EFL learners whose theses and dissertations contained hypes, focusing on the intentions and reasons for using hypes. Hypes were analyzed using Millar et al.’s (2020) functional framework, and reflexive thematic analysis was conducted to identify motivational patterns underlying their usage. Results. Through thematic analysis of in-depth interviews, this study reveals that participants generally viewed hypes positively and strategically used them across all functional categories, with a novel category targeting the research gaps. While viewing hypes positively as persuasive tools, they expressed concerns about appearing overconfident. Five external motivational factors were identified: supervisory expectations shaped by hierarchical power dynamics, audience awareness, AI tool influences, classroom instructions, and published writing conventions. Conclusion. This study demonstrates that Indonesian EFL learners use hypes mainly to meet supervisory expectations rather than publication pressures. The findings offer three key theoretical contributions: first, that hype usage represents identity construction where EFL learners negotiate academic and cultural expectations; second, that power asymmetries in hierarchical context manifest linguistically through rhetorical compliance; and third, that AI tools now function as rhetorical agents alongside traditional human influences in academic discourse socialization.

Background: Artificial intelligence (AI) inaugurates a new educational era that compels a rethinking of established pedagogical paradigms. Within the epistemological uncertainty of the post-truth era, it has reconfigured teacher-student dynamics (TSD) in ways that challenge traditional assumptions about agency and authority in the classroom. Purpose: The article addresses the disruption of TSD under the evolving force of AI, with particular attention to tensions in learner–teacher agency asymmetry in the context of AI-mediated language education. Conceptual Contribution: Positioned within the genre of conceptual scholarship, the article introduces a model that delineates six interrelated dimensions of TSD disruption in the age of AI. The framework does not merely describe emerging shifts but systematizes them into an interpretive structure that traces the trajectory of TSD evolution. In doing so, it foregrounds the broader implications of these transformations for educational policy, pedagogical design, and research agenda in language education. Implications: The analysis contends that AI realities not only govern but also reshape the human texture of pedagogical interaction. Preserving the integrity of the language classroom requires learning designs that foreground dialogic engagement and epistemic trust, while constructively integrating AI innovations. Keywords: Artificial intelligence (AI), ethics, integrity, trust, relationship

Introduction: The existing scholarship related to higher education, while extensive, lacks comprehensive analyses of prevailing research themes. Numerous reviews provide detailed charts of niche topics, yet they fall short of offering a comprehensive overview. To accurately chart the major tendencies within the broader field of higher education, our analysis includes all review types, arguing that they collectively provide a more complete picture and reveal the field’s dominant themes. Purpose: To fix the prevailing themes in research on higher education and research agendas. Method: We employed a systematic scoping review methodology to map the breadth of literature. The review questions were formulated using the PCC (Problem, Concept, Context) framework to ensure a comprehensive scope. The conduct and reporting of this review followed the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). 128 reviews of diverse types published in 2024-2025 were selected from the Scopus database. The included reviews were based on publications within the timeframe from 2010 to 2025. Results: This systematic scoping review reveals that systematic reviews (55%) constitute the dominant methodological approach in higher education research, followed by bibliometric (12%) and scoping reviews (9%). The analysis identifies eight prevailing thematic clusters that directly inform future research agendas, consolidating them into four synthesized directions: technology and digital transformation; curriculum and learning outcomes; equity, ethics, and education's social role; and systemic shifts including internationalization and sustainability. Notably, the field shows concentrated emphasis on artificial intelligence integration and AI competency development, signaling a pivotal direction for future higher education research. Conclusion: This review has synthesized the current intellectual landscape of higher education to map its prevailing themes and, consequently, to define a unified research agenda. The primary implication of this work is to focus the efforts of the research community by highlighting the most pertinent and promising directions for further investigation. Although this review provides valuable insights, its findings are limited by its reliance on a single database and its restriction to English-language reviews, potentially omitting significant literature from regional or non-English sources. Therefore, we recommend future research pursue a more expansive systematic mapping with a longer time span, multiple databases, and incorporate reviews published in various languages to achieve a truly comprehensive perspective.

Background: This study addresses a gap in applied phonetics by developing an evidence-based, neural network-driven computer phonetic test for Russian EFL learners. Integrating interdisciplinary methods, the system targets Russian-specific pronunciation deviations and delivers adaptive feedback, thereby advancing both perception and production skills while aligning technological innovation with pedagogical effectiveness. Purpose: The purpose of the study is twofold: (1) to design and develop a computer-based system employing deep learning neural networks for objectively assessing Russian EFL students’ perception and production skills, and (2) to evaluate the effectiveness and reliability of this system through repeated testing, statistical analysis of learner performance and user feedback. Methods: A pre-test identified frequent segmental deviations, informing a targeted item pool. The software was developed in Microsoft Visual Studio 2022 (C#) using the Microsoft Speech Recognition Engine. The perception module used randomized audio stimuli (WAV files), while the speech recognition one recorded response via built-in microphones for automated accuracy evaluation. Twenty-five Russian EFL students (B1–B2 CEFR, aged 19–22) completed three test iterations at one-week intervals. Post-test questionnaire assessed usability and perceived learning gains. Data were analysed using descriptive statistics and correlation analysis. Results: We designed a computer-based system employing deep learning neural networks and assessed its efficiency in Russian EFL learners. The study found a 14.5% overall improvement in participant performance, with results showing a clear linear increase supported by a high R² value. Students performed better in perception tasks than in production practice. Pearson correlation analysis indicated consistent performance between consecutive attempts, supporting robust test-retest reliability. Both modules showed high internal consistency (α = 0.90 for perception, α = 0.88 for production). Participants rated the tool as useful and interesting, although they suggested improving the speech recognition function due to minor technical flaws. Conclusion: The module focused on testing perception skills can serve as an effective and engaging learning tool. While the pronunciation control component shows potential, its performance can be further enhanced through additional testing with high-sensitivity microphones to refine speech recognition accuracy. Overall, continued exploration of CAPT systems presents a promising direction for future research and innovation.