The purpose of this article is to demonstrate the pedagogical application of Generative AI (GenAI) to a particularly fruitful area of Homer, the speeches, drawing on narratology as a theoretical framework to contextualise the use of the technology. The teaching methodology, students interrogating a chatbot to explore a speech from Homer, comprises dialogic learning as students craft questions, reflect and respond to the chatbot’s responses. This reiterative process is demonstrated through dialogue with Microsoft Copilot on one speech from the Iliad, book 16, where Achilles chides a tearful Patroclus (Il. 16.7–19), and one from the Odyssey, book 19, where Odysseus rebukes the treacherous maid Melantho (Od. 19.71–88). Two different strategies were deployed to highlight the response patterns of GenAI. With the Iliad, the strategy was to ask Copilot questions directly about the speech; with the Odyssey, Copilot was asked to assume the role of a character from the exchange. It was found that Copilot supported a narratological interpretation of the text by offering students an informed, and largely accurate, window on the speech for them to explore key considerations such as focalisation, the viewpoint on the unfolding narrative. Furthermore, while Copilot provided a rich layered response, there was still space for students to negotiate the meaning of the text further, retaining their own responsibility as active learners. The conclusion is that GenAI is in line with an inquiry-based approach to the study of Homer that promises to engage students and keep the discipline fresh.

The rise of Generative AI has accelerated the shift toward Industry 5.0, marking a critical transition from the technology-centric focus of Industry 4.0 to a human-centric, value-driven paradigm. While its predecessor prioritized automation and technology, Industry 5.0 integrates advanced human–machine collaboration with social imperatives to create resilience. This study advances current literature by presenting a novel systems-based framework, grounded in systems theory and legitimacy theory, which conceptualizes Industry 5.0 as an interconnected ecosystem rather than isolated pillars. We identify technological adaptation, specifically AI integration, and human-centricity as critical inputs that drive economic, environmental, and social sustainability as systemic outputs. By mapping these interdependencies, the model demonstrates how cohesive components collectively fuel organizational transformation. These findings offer actionable insights for aligning corporate strategies with Sustainable Development Goals, providing policymakers and practitioners with future-oriented pathways to navigate this complex, emerging industrial environment.

The 2025 HTAi Global Policy Forum (GPF) report offers a timely and thoughtful synthesis of the opportunities and challenges associated with the use of artificial intelligence (AI), and particularly generative AI (GenAI), in Health Technology Assessment (HTA) (1). Its emphasis on trust, human agency, and risk-based approaches reflects both the maturity of the discussion within the HTA community and a shared recognition that technical capability alone is insufficient for responsible adoption. The report succeeds in articulating a common set of principles and a broadly aligned vision across HTA bodies, life sciences, and other interest holders.

Generative AI (GenAI) offers potential for English language teaching (ELT), but it has pedagogical limitations in multilingual contexts, often generating standard English forms rather than reflecting the pluralistic usage that represents diverse sociolinguistic realities. In response to mixed results in existing research, this study examines how ChatGPT, a text-based generative AI tool powered by a large language model (LLM), is used in ELT from a Global Englishes (GE) perspective. Using the Design and Development Research approach, we tested three ChatGPT models: Basic (single-step prompts); Refined 1 (multi-step prompting); and Refined 2 (GE-oriented corpora with advanced prompt engineering). Thematic analysis showed that Refined Model 1 provided limited improvements over Basic Model, while Refined Model 2 demonstrated significant gains, offering additional affordances in GE-informed evaluation and ELF communication, despite some limitations (e.g., defaulting to NES norms and lacking tailored GE feedback). The findings highlight the importance of using authentic data to enhance the contextual relevance of GenAI outputs for GE language teaching (GELT). Pedagogical implications include GenAI–teacher collaboration, teacher professional development, and educators’ agentive role in orchestrating diverse resources alongside GenAI.

This paper explores the transformative potential of artificial intelligence (AI), particularly generative AI (GenAI), in supporting the teaching, learning, and assessment of second language (L2) listening and speaking. It examines how AI technologies, such as spoken dialogue systems and intelligent personal assistants, can refine existing practices, offer innovative solutions, and address challenges related to spoken language competencies, as well as drawbacks they present. It highlights the role of GenAI, explores its capabilities and limitations, and offers insights into the evolving role of GenAI in language education. This paper discusses actionable insights for educators and researchers, outlining practical considerations and future research directions for optimizing GenAI integration in the learning and assessment of listening and speaking.

Recent developments in artificial intelligence (AI) in general, and Generative AI (GenAI) in particular, have brought about changes across the academy. In applied linguistics, a growing body of work is emerging dedicated to testing and evaluating the use of AI in a range of subfields, spanning language education, sociolinguistics, translation studies, corpus linguistics, and discourse studies, inter alia. This paper explores the impact of AI on applied linguistics, reflecting on the alignment of contemporary AI research with the epistemological, ontological, and ethical traditions of applied linguistics. Through this critical appraisal, we identify areas of misalignment regarding perspectives on knowing, being, and evaluating research practices. The question of alignment guides our discussion as we address the potential affordances of AI and GenAI for applied linguistics as well as some of the challenges that we face when employing AI and GenAI as part of applied linguistics research processes. The goal of this paper is to attempt to align perspectives in these disparate fields and forge a fruitful way ahead for further critical interrogation and integration of AI and GenAI into applied linguistics.