9INTRODUCTION Rethinking English Studies Through AI: Challenges, Ethics, and Innovation 1 Introduction Over a relatively brief period, the rapid development of artificial intelligence (AI) has reshaped our perception of traditional concepts that have been with us for decades, some even for centuries. Communication, language, academic writing, translation, and education have not escaped that transformation. Generative AI tools, particularly chatbots that use large language models (LLMs) to generate tailor-made texts, presentations, images, and videos, have entered the classroom, academic research, and various professional settings – often faster than our pedagogical frameworks and ethical standards can adapt. As these technologies grow more sophisticated and accessible, it has become impossible for the various fields of English studies – encompassing linguistics, writing, teaching, and translation – to ignore their presence and avoid their impact on all aspects of the fields. This thematic issue of ELOPE responds to this phenomenon. It brings together eleven original research articles that critically and creatively engage with the implications of AI for English language use, learning, and mediation. The volume addresses a range of issues and contexts: from pragmatic annotation in literary texts, to metaphor translation in the EFL classroom, and from ESL writing development, to teacher perceptions of AI tools. The contributions draw the reader’s attention to both the advantages and the pitfalls of integrating artificial intelligence into the various fields of English studies. The influence of AI on education is undeniably evident on many levels of teaching and learning. One of the salient aspects is its ability to individualize instruction: AI-driven platforms analyse each learner’s performance and recommend tailor-made exercises, readings, or feedback, with which – according to Msambwa, Wen and Daniel (2025); and Massaty, Fahrurozi and Budiyanto (2024) – we can sustain motivation and enhance progression. Another function of AI systems involves fostering critical present-day skills like computational thinking, or complex problem-solving, by using modern teaching or learning approaches such as just-in-time guidance or scaffolded challenges, i.e., problems structured to gradually increase in difficulty or complexity, with support (or scaffolding) provided along the way (Massaty, Fahrurozi, and Budiyanto 2024). Moreover, AI has expanded access to automated analysis and language support (Krishnan and Zaini 2025), which is not limited to English studies but interconnects with other disciplines. AI can also create collaborative learning environments by moderating group discussion, supporting peer-to-peer interaction, and converting static materials into adaptive simulations (Msambwa, Wen, and Daniel 2025; Orlanda-Ventayen 2024). Kusmiadi and Wahyudin (2024) also report that behind the scenes, administrative activities like grading, attendance monitoring, and early-alert systems are increasingly automated, supposedly making more time for the teaching staff to focus on innovative curriculum design and individualized mentorship. Tomaž Onič, Mladen Borovič, David Hazemali University of Maribor, Slovenia 10 Yet all these improvements open new, relevant considerations. Apart from the privacy and security issues raised by Yu et al. (2024) or Asad et al. (2024), which accompany the collection and analysis of student data, the use of AI opens the door to algorithmic biases that can skew recommendations, potentially privileging certain learners while marginalizing others (Cui and Alias 2024). Researchers also suggest caution in overreliance on AI, which can cause an eventual decrease in deeper cognitive engagement, as students elect to leave critical thinking to the machines (Butson and Spronken-Smith 2024; Castillo-Martínez et al. 2024). Ethical questions regarding authorship and academic integrity further complicate AI’s role in writing and assessment (Floridi 2023; Butler and Jiang 2025). These challenges are especially acute where limited digital infrastructure and low digital literacy might increase existing inequalities (Asad et al. 2024; Nguyen and Hoang 2025). In research contexts, AI accelerates the process by analysing large bodies of data, from historical archives to learner datasets, to identify patterns that are – owing to dataset size – potentially beyond human grasp (Cui and Alias 2024; Kusmiadi and Wahyudin 2024). In their study based on historical document analysis, Hazemali et al. (2024) demonstrated that AI excels at select surface-level processing and data extraction, but falters on tasks demanding interpretation, context sensitivity, or inference. Additionally, AI-assisted writing tools streamline drafting, editing, and literature synthesis, yet they require careful human oversight to maintain scholarly rigor and guard against “black-box” errors, suggested by Castillo-Martínez et al. (2024) and Ramirez and Esparrell (2024). These capabilities support new methodologies based on (big) data, such as adaptive experimental designs, large-scale sentiment analyses, and interdisciplinary collaborations (Jacques, Moss, and Garger 2024; Orlanda-Ventayen 2024). Yet they also open methodological and ethical questions: how can we assure replicability if algorithms continually develop and change? Who merits authorship credit for AI-(co-)authored output? To what extent must AI’s internal logic be disclosed, particularly when privacy or intellectual property are at stake (Butson and Spronken-Smith 2024; Yu et al. 2024)? As we can see from this review of recent educational and research development, there exists an urgent need for comprehensive ethical and policy frameworks. Institutions must balance AI-mediated automation with rigorous human oversight to protect privacy and academic integrity (Floridi 2023; Ali et al. 2024; Yu et al. 2024), while at the same time, they must promote training in digital literacy and ensure that the benefits of AI are not limited to small groups of learners and researchers but are accessible to all (Kusmiadi and Wahyudin 2024; Yu et al. 2024), which is one of the crucial tasks of the humanities in the digital world. 2 Overview of the Studies The articles in this issue are grouped into four thematic clusters – Language, Academic Writing, English Language and Literature Teaching, and Translation Studies – each addressing a particular aspect of AI and its growing role in our work. The boundaries between disciplines are, of course, neither strict nor hermetically detached from other fields, since the issues often venture into interdisciplinary areas. The present volume offers an insight how scholars, educators, and practitioners can engage with AI not merely as a tool, but as a stimulus for rethinking core assumptions and professional practices in English studies. T. Onič, M. Borovič, D. Hazemali Rethinking English Studies Through AI: Challenges, Ethics, and Innovation T. Onič, M. Borovič, D. Hazemali 11INTRODUCTION 2.1 Language: AI as a Tool for Language Analysis The first two articles investigate the application of generative AI in linguistic analysis. The opening study by Tadej Todorović, Andrej Flogie and Daniel Hari tests ChatGPT, Gemini, and DeepSeek for speech act classification in Harold Pinter’s The Birthday Party. With an accuracy of 82% under optimized conditions, the results affirm AI’s potential for supporting discourse annotation – particularly when prompts are paired with theoretical grounds, a practice increasingly advocated in AI-assisted humanities research (Lozić and Štular 2023). The second article by Agata Križan  and Aja Barbič applies Martin and White’s appraisal framework to AI-generated analysis of evaluative language. The coding results done by ChatGPT and Microsoft Copilot were compared and then supervised by human analysts, revealing an encouraging overlap in basic categorization but a lack of nuance in AI-generated responses. This reflects a recurring challenge in AI-driven textual analysis: the tendency to prioritize formal correctness over content accuracy or critical precision (Gonzalez Garcia and Weilbach 2023). 2.2 Academic Writing: Supporting Writing with AI Three articles address AI’s impact on student writing and engagement. In the first one, the author Silvana Neshkovska reviews literature on ChatGPT’s role in academic writing. While highlighting benefits in autonomy and motivation, the study warns against the ethical pitfalls of AI overuse. The blurred lines between assistance and authorship remain a pressing concern, particularly in educational contexts where writing is also a process of knowledge construction (Altmäe, Sola-Leyva, and Salumets 2023; Abadie, Chowdhury, and Mangla 2024; Asad et al. 2024). The second article in this section by Rashmika Lekamge and Clayton Smith explores how learners of English as a Second Language (ESL) interact with auto-correction tools like the one provided in Microsoft Word. While the tools reduced surface-level errors, extended reliance on the tool led to lower self-editing skills and writing confidence – a dynamic mirrored in recent AI-based writing support tools (Kasneci et al. 2023; Kohnke, Zou, and Su 2025). In a study of Indonesian university students, the authors Tommy Hastomo, Andini Septama Sari, Utami Widiati, Francisca Maria Ivone, Evynurul Laily Zen, and Muhammad Fikri Nugraha Kholid show that chatbot engagement, particularly behavioural and cognitive, correlates with improved English proficiency. This confirms emerging research suggesting that AI tools can support language acquisition and enhance vocabulary, grammar, and writing fluency if engagement is active, reflective, and task-focused (Ali et al. 2024; Krishnan, and Zaini 2025). 2.3 English Language and Literature Teaching: Teacher Attitudes, Competence, and Professional Development In this section, two studies explore how language educators respond to AI in the classroom. A survey conducted by Saša Jazbec, Bernarda Leva and Marta Licardo among Slovenian teachers 12 T. Onič, M. Borovič, D. Hazemali Rethinking English Studies Through AI: Challenges, Ethics, and Innovation finds that while AI is mostly not viewed as a threat, it is seen as a disruptor – requiring shifts in instructional design and professional identity. This echoes recent concerns about the social and psychological effects of AI in education (Suchithra and Arya 2025; Kasneci et al. 2023) and is consistent with Krishnan and Zaini’s (2025) conclusion that AI’s potential can be realized only when educators are well-trained and supported in its use. Croatian EFL teachers Bojan Prosenjak  and Eva Jakupčević likewise reveal mixed levels of digital competence. Professional development is therefore essential – not only for skill- building but for helping educators and pre-service teachers form balanced, critical views of AI. This same goal is reinforced by Butler and Jiang (2025), who found that less confident users of ChatGPT were more likely to accept its output uncritically. 2.4 Translation Studies: Exploring AI’s Role in Language Mediation The final section, containing four articles, examines translation issues and practices in the new context of AI presence. In the first of the four articles, Nataša Gajšt examines business correspondence translated with the help of ChatGPT, Claude, and Gemini. The author concludes that while the output was mostly usable, inconsistencies in tone and register demonstrate the need for human editorial judgment – a finding echoed in other recent research not specifically in the area of translation (e.g., Hazemali et al. 2024). Another study by Simon Zupan, Zmago Pavličič and Melanija Larisa Fabčič explores machine translation of nominal phrases in technical texts. With nearly half the phrases mistranslated, the study exposes the limits of current LLMs in high-density, context-dependent language – a familiar challenge in AI language models that, according to Boros et al. (2024), still struggle with specialized corpora. Unsurprisingly, metaphor translation presents another difficulty that AI cannot yet successfully address or resolve. While students in the experiment appreciated using AI tools, errors in figurative language revealed their limitations. The author Marija Brala Vukanović, however, argues that these inaccuracies can be turned into didactic benefits under the guidance of a skilled teacher. The section closes with a study on AI-powered transcreation in cross-cultural marketing. Surprisingly, the authors Ghodrat Hassani, Marziyeh Malekshahi and Hossein Davari find that trained students outperformed professionals after using ChatGPT tools, which underlines the importance of quality prompt engineering and guided learning for an optimal outcome. According to Gonzalez Garcia and Weilbach (2023), this is particularly relevant in domains where cultural resonance is as crucial as linguistic accuracy. 3 Conclusion The contributions to this special issue collectively show that artificial intelligence is no longer a peripheral novelty but a pertinent phenomenon that has already won a visible position in English studies. We can expect its relevance and status to grow stronger and more central in the future, regardless of the discipline or subfield of English studies, which is reflected in these 13INTRODUCTION articles that offer both a critical and constructive account of AI’s growing influence. Apart from this general understanding, the studies reach another shared conclusion, which is that AI tools are only as effective and ethical as the human users who operate them, and as they do so they rely on their own expertise and ethics. It is therefore crucial to strive for thoughtful and responsible integration of AI in academic and professional work. This issue of ELOPE does not seek to offer final answers but rather to open new questions and inquiries. Teachers, researchers, translators, and others who deal with English studies are uniquely positioned to shape the newly emerging relationship between language and technology. The questions raised here – about accuracy, agency, pedagogy, and professional roles – will continue to define our fields in the years ahead. It is our hope that this collection provides a valuable foundation for those navigating, critiquing, and contributing to the future of AI in English language studies. 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