Jul 2026· Arab World English Journal· Vol 17, pp. 400-417· 0 citations· 17 references
TL;DR
The study argues that this suggests the need for formal AI policy, and for teaching AI-critical genre literacy in English for Specific Purposes education, and for teaching AI-critical genre literacy in English for Specific Purposes education.
Abstract
Task-based language assessment assumes that the learner’s performance reflects the learner’s own developing communicative competence. Generative AI challenges this assumption by allowing students to delegate task completion to an external system, thereby threatening task authenticity, performance authenticity, and the interpretability of form. The study aims to examine how 40 university English for Specific Purposes teachers at three Ukrainian universities experience the impact of generative artificial intelligence on the task-based language teaching and assessment validity chain. This issue is significant since, in the absence of institutional AI policy, teachers are the primary regulators of AI use in assessed contexts, and their decisions are the primary mechanism through which validity is maintained or compromised, yet remain empirically under-examined. The study employed a qualitative approach, including analytical content analysis of open-ended survey responses and structured classroom observations of 12 teachers (a purposive subsample). The results were five-fold: teachers found artificial intelligence useful for task material generation and formative feedback; teachers had consistent validity concerns for summative assessment; formative-summative asymmetry emerged in different disciplinary clusters; teachers independently indicated that students’ output converged towards a single artificial intelligence-modified output; and students would not be able to critically evaluate the artificial intelligence-generated text. Our findings are interpreted using the Task-Based Language Teaching-Assessment-Artificial Intelligence Nexus, a preliminary heuristic model that identifies three interconnected nodes of validity threat. The study argues that this suggests the need for formal AI policy, and for teaching AI-critical genre literacy in English for Specific Purposes education.
Generative artificial intelligence (GenAI) has been primarily framed as an impartial educational tool. However, this framing overlooks an even larger shift: the reassignment of epistemological authority from teachers to students to machines. This paper presents a conceptual evaluation of the extent to which GenAI redistributes students'and teachers'ability to act in classrooms to produce knowledge, validate each other's claims, and create evidence of student learning while collaborating with and competing against humans. This evaluation draws on various theoretical paradigms, including Distributed Agency, Self-Determination Theory, Society 5.0, and Technology Integration Paradigms, including TPACK and SAMR. While all of the theoretical paradigms evaluated are relevant to the role of agency within education mediated by AI, none of them address the ongoing disparity regarding equitable distribution of power, ownership of the data used to mediate interaction, and accountability in relation to human-mediated interactions. As such, this paper introduces the Ecological Co-Agency Framework, which defines agency in terms of relational, regulatory, and pedagogical processes, conditioned by a defined commitment to human accountability for epistemological claims.
The massive spread of Generative Artificial Intelligence (Gen AI) into language classrooms has reopened a currently relevant question related with the human teacher’s role, and in some quarters revived the worry that the instructor who once stood as the “Sage on the Stage” has become obviously less important. This narrative literature review asks what actually happens to the roles and professional identities of language teachers as Gen AI has massively intervened their work, with particular attention to English for Specific Purposes (ESP). Following a protocol-guided search of academic databases, thirteen peer-reviewed studies published between 2022 and 2026 were synthesized, read alongside a smaller body of abundant literature on general language teaching. Taken together, the studies point away from displacement and toward reinvention. Teachers, ESP practitioners in particular, are taking on the work of prompt design, drawing on what one study terms AI-pedagogical knowledge to translate tacit expertise into instructions a model can follow. Since Gen AI can produce credible but incorrect content in specific fields such as engineering, medicine, law, etc., ESP teachers also find themselves play roles as domain validators and as guides to critical AI literacy. What emerges is less an authoritative source of knowledge than a facilitator who decides when to lean on automation and when to rely on judgment, context, and rapport that a model cannot supply.
Gregorius Punto Aji, Angelina Kusuma Jelita Mawarni· International Journal of Edu...· 0 citations
The rapid integration of artificial intelligence (AI) into educational contexts has prompted urgent reconsideration of established pedagogical frameworks, particularly within the domain of English Language Teaching (ELT). While AI-powered tools are increasingly present in language classrooms, the theoretical foundations needed to guide their thoughtful and equitable integration remain underexplored. This paper examines the reconceptualisation of ELT in the age of AI through a multi -theoretical lens, drawing on constructivism, Vygotsky's sociocultural theory, connectivism, the Technology Acceptance Model (TAM), and Communicative Language Teaching (CLT). The central argument advanced is that no single theory adequately accounts for the complexity of AI -mediated language learning; instead, an integrated framework is needed, the one that positions the teacher as a critical mediator, foregrounds social interaction as the locus of language developmen t, and embraces networked knowledge as a legitimate epistemic resource. The paper proposes a conceptual model; the AI-Mediated Language Learning Model (AMLL) to map the dynamic relationships among AI tools, teacher agency, student interaction, and language development. Implications are drawn for classroom practice, institutional policy , and future research directions. It is argued that the future of ELT must be neither technophilic nor technophobic, but critically reflexive.
Y. Y. Adam· Australian Journal of Busine...· 0 citations
Modern foreign language (MFL) teaching is informed by cognitive schemas, ideological discourse and technology-mediated instructional approaches, but these dimensions are often addressed separately. This study set out to explore how cognitive, ideological, and technical architectures affect experiences of language learning in the current digital environment and to advance an integrated model that repurposes language teaching as a multi-layered semiotic system. A mixed-methods design was employed using a tri-varied research framework that combined Cognitive Linguistics Analysis, Critical Discourse Analysis, and Technology-Enhanced Learning. Data were obtained from 120 hours of classroom discourse, 86 teaching materials, and 214 student responses collected between 2024 and 2026. Results reveal that classroom language is influenced by implicit mental schemas, discursive power relations, and algorithmic mediation. Quantitatively, the density of conceptual metaphors in classroom discourse positively correlated with learner understanding (r = .62, p < .05). Teacher explanation, measured by time spent on didactic instruction, decreased student engagement (β = .01), while dialogic discourse, measured by the frequency of teacher-student exchanges, increased engagement (β = .36, p < .05). Conversely, prescribed use of modality showed a negative association with engagement (β = -.29, p < .05). At the qualitative level, modality, transitivity and evaluative language indexed authority and transmitted vertical epistemic relations of knowledge. The AI-mediated feedback also exhibited tendencies toward standardisation; for instance, 83% of non-standard language forms were marked as incorrect. The authors argue that cognitive, ideological and technological dimensions are co-constitutive rather than separate. It has to do with more critical, responsive and equitable language pedagogy.
Iskandarsyah Siregar· Journal of Languages and Lan...· 0 citations
The rapid integration of generative artificial intelligence (GenAI) tools as reshaped how English for Specific Purposes (ESP) students —like in engineering disciplines— approach language production. Despite their growing presence, limited research has examined the risk of overreliance on GenAI and its potential to diminish students’ critical language awareness. Using a purposive sampling in which 27 students who met the criteria, the study addressed three questions: (1) To what extent do ESP students critically evaluate AI-generated suggestions? (2) What factors influence their acceptance, revision, or rejection of AI output? and (3) In what ways do ESP students utilize AI tools to support their English skills? A qualitative approach was utilized with the data collected through class observation and open-ended questionnaires. The findings revealed that while AI use is widespread, many students demonstrated critical engagement, often modified sentence structures to sound more humanized. However, lack of confidence in their English abilities and time constraints were key reasons for passive acceptance of AI output. Students used AI mostly for overcoming their lexical barriers. These findings underscore the need for careful integration of GenAI into ESP classrooms, supported by targeted instruction and thoughtful lesson design, particularly for students with lower proficiency. Future research should further examine patterns of overreliance across more diverse learner populations.
M. Ajiza, I. Yuliasri, Hendi Pratama et al.· English Review: Journal of E...· 0 citations
Generative artificial intelligence (GenAI) tools increase the speed and polish of academic writing, but they also destabilise higher education’s reliance on written artefacts as proxies for understanding. This raises a risk that students can submit fluent text while bypassing the effortful discomfort through which information is usually turned into durable knowledge and judgment. This article synthesises how GenAI-enabled cognitive bypass is described in recent scholarship and identifies conditions under which GenAI use shifts from a cognitive extender to a cognitive substitution. This conceptual article bridges existing theories across disciplines to broaden our understanding of GenAI’s role in students’ cognitive development. Firstly, epistemic hollowing occurs when students are seduced by the fluency of large language models’ (LLMs) outputs, leading to weakened verification processes. Secondly, the uncritical use of GenAI may erode desirable difficulties, and retrieval practices may substitute for key cognitive operations. Thirdly, universities’ audit culture and product-oriented assessment regimes make cognitive bypass rational. Cognitive bypass is framed as a mismatch between tool affordances, learner goals and institutional proxy systems. The implications point towards teaching, assessment and supervision designs that validate processes, require defence and verification and preserve epistemic agency in GenAI-saturated universities.
Contribution: The study integrates fragmented GenAI in higher education debates into a three-lens explanatory model of cognitive bypass and translates it into process-oriented implications for teaching, assessment and postgraduate supervision.
Marcina Singh· Transformation in Higher Edu...· 0 citations