Jul 2026· Region - Educational Research and Reviews· Vol 8, pp. 22· 0 citations· 2 references
TL;DR
The AI-mediated knowledge construction (AMKC) framework is proposed to explain how GenAI may support graduate students' reading-to-write development and provides a theoretically grounded account of GenAI-mediated academic literacy development in higher education.
Abstract
Generative artificial intelligence (GenAI) research has largely focused on text generation, feedback, and writing enhancement, overlooking the cognitive and epistemic processes underlying academic knowledge construction. This paper proposes the AI-mediated knowledge construction (AMKC) framework to explain how GenAI may support graduate students' reading-to-write development. Integrating academic literacies, reading-to-write research, sociocultural theory, and dialogic approaches, the framework positions GenAI as a cognitive mediator and dialogic partner across dialogic reading, knowledge transformation, source integration, disciplinary meaning-making, critical reflection, and academic writing. Six theoretical propositions elaborate the mechanisms of AI-mediated literacy development, while pedagogical implications and a future research agenda address implementation and empirical validation. By shifting attention from writing assistance to knowledge construction, AMKC provides a theoretically grounded account of GenAI-mediated academic literacy development in higher education.
Traditional second language (L2) writing instruction and assessment frequently emphasize unaided, timed production, a model that no longer fully represents the communicative realities of AI-mediated contexts. This conceptual article aims to reconceptualize the L2 writing construct for educational settings in which generative AI is routinely and legitimately used. The study uses a theory-driven integrative conceptual synthesis. Sources were located through purposive searching of Scopus, ERIC, Web of Science, and Google Scholar, supplemented by citation chaining and journal hand-searching, and screened against stated inclusion criteria across two streams: foundational scholarship on mediated cognition, genre, literacy, and validity, and work on generative AI and writing published from 2020 onward. Forty-seven sources were retained for close analysis, spanning sociocultural learning theory, activity theory, distributed cognition, multiliteracies research, computer-assisted language learning, and language assessment scholarship. Analysis proceeded through manual thematic coding of construct-relevant claims, conducted by the first author and independently reviewed by the second. The resulting orchestration model defines AI-mediated writing as the purposeful coordination of human judgment with machine-generated output under conditions of authorial responsibility. It specifies four interdependent competencies: prompting, critical evaluation, adaptation, and ethical accountability. The analysis shows that traditional dimensions of writing, including coherence, organization, language use, critical thinking, and audience awareness, are not displaced by AI-mediated writing but redistributed across these competencies. The paper also identifies specific challenges for L2 writers, especially the difficulty of evaluating and reshaping fluent AI-generated output in a language still being acquired. The article recommends process-visible assessment designs, genre-specific orchestration tasks, and empirical validation studies that examine construct structure, scoring reliability, and consequential validity.
M. Askari, A. Rahim· Polyglot: Journal of Linguis...· 0 citations
Generative artificial intelligence (GenAI) is rapidly changing professional language education, yet the behavioral and cognitive mechanisms through which learners engage with GenAI-mediated language tasks remain insufficiently synthesized. This critical integrative review reframes translation and interpreting education as a high-cognitive-load case of professional language learning in which students must evaluate AI output, regulate feedback use, and remain accountable for meaning across languages. A documented evidence-selection workflow was applied to an author-curated bibliographic corpus and a supplementary source-expansion corpus comprising 690 potentially relevant records, from which 21 core studies were selected for focused synthesis. The included studies directly addressed GenAI, translation/interpreting education, AI-generated feedback, post-editing, learner revision, interpreter assessment, or AI literacy. The synthesis is theoretically grounded in cognitive load theory, self-regulated learning, feedback literacy, trust in automation, and social-cognitive accounts of agency. Across the reviewed evidence, learners’ engagement with GenAI involves cognitive load redistribution, trust calibration, feedback uptake, metacognitive monitoring, affective responses, and behavioral revision decisions. The review proposes a behavioral model that links technological mediation, cognitive appraisal, self-regulated behavioral engagement, pedagogical regulation, observable process evidence, and professional agency. We argue that the central educational challenge is not whether GenAI improves single-task language performance, but how learners develop calibrated trust, critical judgment, self-regulated feedback use, and professional agency in human–AI language-learning environments. The framework may also inform adjacent AI-mediated language-learning contexts where learners must evaluate feedback, revise language, and justify communicative choices.
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
It is suggested that AI can enhance drafting, revision, and feedback processes, improving coherence, metacognition, and writing confidence, however, these benefits are accompanied by persistent concerns regarding ethical ambiguity, inconsistent policy guidance, and insufficient faculty training.
Samira Dichari, Fadi Jaber· Journal of Education and Tra...· 0 citations
The increasing availability of generative artificial intelligence (AI) tools has introduced new possibilities for transforming how students interact with academic texts. While most educational discussions have focused on AI-assisted writing and assessment, the role of AI in supporting reading engagement remains underexplored. This study presents a narrative literature review examining the relationship between generative AI, multimodal learning, and reading engagement in higher education. Relevant studies were identified through academic databases using keywords related to generative AI in education, multimodal learning, and reading engagement, and were analyzed through thematic categorization. The review indicates that AI-mediated text transformation, particularly converting text into visual or animated representation, may restructure reading activity into an iterative process involving rereading, verification, and adjustment. Such interaction aligns with cognitive, behavioral, and affective dimensions of engagement by encouraging learners to compare generated representations with original textual sources. Rather than functioning as an automated comprehension tool, generative AI can act as a mediating learning artefact that supports active involvement with reading material when pedagogically guided. The study proposes a conceptual framework describing AI-assisted text-to-animation learning as a cyclical engagement process. The findings suggest that the educational value of generative AI depends on instructional design that promotes critical interaction with generated output. Future research should empirically investigate classroom implementation to validate the proposed framework.
Sulfaedah Lestari· ELS Journal on Interdiscipli...· 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