• Journal Menu
  • About This Journal
  • Aims and Audience
  • Abstracting/Indexing
  • Editorial Board
  • Subscription Information
  • Contact Information
• Authors
  • Author Guidelines
  • Code of Conduct
  • IJLS Editorial Style
  • IJLS Style Sheet
  • Peer Review Process
  • Call for Papers
  • FAQ
• Editors
  • Editorial Board
  • Reviewer Guidelines
  • Code of Conduct
  • Peer Review Process
  • IJLS Editorial Style
• Archives
  • Papers in Press
  • Latest Issue
  • Back Issues
  • Special Issues
• Home

This special issue delves into the influence of artificial intelligence on discourse studies, focusing on its effects on linguistic research, education, and academic communication. AI-driven tools, particularly those in natural language processing, have significantly improved the efficiency of text analysis through methods like sentiment analysis, topic modeling, and automated coding. However, challenges persist, such as AI’s struggles with contextual understanding and ethical concerns related to bias and misinformation. Drawing from applied linguistics, pedagogy, and computer science, this issue critically explores how AI shapes digital language practices, academic authority, and knowledge creation. By combining theoretical insights with empirical studies, this collection sheds light on both the benefits and limitations of AI, encouraging researchers and educators to approach these technologies thoughtfully while making the most of their potential in discourse analysis and language studies.

This study addresses similarities and differences as to how three chatbots—namely ChatGPT, Bing Chat and Google Bard—‘converse’ with humans; in so doing, I also analyze to what extent the answers yielded by chatbots may differ from the typical answering patterns featured in human-to-human interviews. To carry out the study, I rely on a corpus specifically compiled to comprise dialogical interactions between AI and humans. Data show that these three chatbots exhibit both similarities in the way their training has been improved since the debut of ChatGPT in 2022, and differences in dealing with their interlocutors. Specifically, Bard’s replies point to a chatbot which accompanies the interlocutor without imposing itself; Bing Chat favours impartiality and neutrality and avoids falling into the trap of problematic conversation; finally, ChatGPT acts like a tutor that leads the way, rather than simply help.

The advent of generative AI is bound to revolutionize professional writing practices. This will inevitably have an impact on writing pedagogies, particularly in the ESP writing courses where speakers of English as a second language are taught to master the principles of domain-specific communication alongside general language skills. While concerns have been raised as to the future of professional writing and writing pedagogy in the era of AI, this article argues that learning to use generative AI tools offers an unprecedented opportunity for students to develop discourse analytical skills that have traditionally been sidelined in ESP genre teaching as they pertained more to the writing process than to the product, the latter taking typically priority—in the minds of students and teachers alike—over the former. This is a welcome development which does justice to the principles of genre pedagogy.

Artificial intelligence (AI) has recently expanded its applications to almost all areas of human activity. Its ability to identify and predict language patterns has transformed academic and professional writing by empowering the creation, editing, and translation of texts. The article looks at the impact of AI in the context of specialized writing studies. After a brief overview of the literature on AI as a professional and learning tool, the article focuses on AI use in the various phases of (teaching) writing. The results of a small survey on the attitude of a group of students towards AI are presented in section 4, exploring both challenges and opportunities of using GPTs as resources for the teaching and learning of writing. Creativity and accuracy are seen as key issues. The chapter closes with brief conclusions on the role of AI in specialized writing.

This position paper explores how corpus linguistics can respond to advancements in AI technologies, including large language models (LLMs). Drawing on theoretical debates about meaning creation, it examines two models of human-computer interaction, namely Turing’s imitation game, which suggests functional equivalence between successful computer and human communication, and Searle’s Chinese room argument, which critiques the idea of genuine understanding by machines. These discussions emphasize the distinction between form and meaning, with meaning grounded in social context and human interaction. The paper then introduces #LancsBox X, a free software tool developed as a response to the challenges of emerging technologies. Designed for efficiency and user experience, #LancsBox X provides advanced functionalities for corpus analysis, including concordancing, collocation analysis and visualization, keyword identification and web scraping. By emphasizing context in language use, the tool demonstrates how computational methods can support linguistic research while addressing the evolving demands of big data analysis.

This paper analyzes the Instagram and website visual content posted by three popular English-speaking tourism boards, namely Tourism Ireland, Destination Canada, and Tourism Western Australia, with a semiotic approach involving manual annotation. By developing a framework integrating empirical multimodality, Systemic Functional Linguistics and corpus studies, the paper shows how data-driven, customized taxonomies may foster evidence-based discourse interpretations and help develop analytical skills to actively interpret signs and decode purposes, which is key in an increasingly artificial context in which algorithms organize and create data through automatic entity recognition. Specifically, results reveal that Instagram imagery prioritizes emotional engagement over concrete information, promoting instant gratification and escape through highly positive, visually appealing positive expectations about leisure experiences of passive contemplation and extra-ordinary, peaceful solitary fruition of nature. This emphasis impacts digital literacy and fosters a consumerist mindset within neoliberal societies, highlighting the necessity for a systematic and critical engagement with visual narratives.

Artificial Intelligence, particularly language models like ChatGPT, is revolutionizing text generation. This study investigates the risks of integrating AI-generated content in journalism. A pipeline scrapes articles from seven newspaper websites, aggregating and summarizing data. Analysis of AI-generated summaries reveals two key risks: subtle biases that challenge journalistic integrity and objectivity, and the absence of clear informational hierarchy. The research examines content nuances, style variations, and potential biases in AI-generated text. Findings provide critical insights into the challenges of using machine-generated content in journalism, contributing to a deeper understanding of AI's implications in creative text generation and offering guidance for responsible integration of these technologies in journalistic practices.

To be operational, Artificial intelligence (AI) uses large language models (LLMs) in the domain of Natural Language Processing (NLP). A significant advancement in this field is Transformer architecture, a deep learning model initially developed for machine translation. The advent of AI conversational interfaces, such as chatbots, has triggered scientific, philosophical, political, social, ethical and legal debates about the pros and cons of AI for humanity, also focusing on the ‘ability’ of AI to accurately translate a wide range of (specialised) texts. These chatbots, powered by LLMs trained on extensive data sets from books and the Internet, are designed to generate coherent and contextually relevant text by understanding and predicting language patterns. Hence, after a diachronic overview of AI and automatic translation, the aim of this paper is to show how chatbots “translate” specialized texts, comparing different chatbots to identify their similarities and differences. While Neural Machine Learning (NML) shows promise, it still struggles to produce consistently satisfactory results.

The application of Artificial Intelligence (AI) tools is having a profound impact on many areas of our lives. This article focuses on communication, an area that cuts across disciplines and professions and is crucial for the promotion of tourism destinations. More specifically, this study explores the opportunities that AI offers in the translation of metaphors in tourism discourse, where they convey cultural and emotional meanings and are crucial for appealing to international audiences. This study builds on previous research (Spinzi, 2024) on the translation of metaphors in sustainable tourism discourse and aims to explore AI tools, specifically neural machine translation (NMT) systems, for their translation, with particular reference to GPT-4, the most advanced iteration of GPT. It evaluates AI-driven metaphor translation, its efficiency compared to human translators and examines its impact on intercultural communication in the tourism sector.