Aug 2026· Polyglot: Journal of Linguistics, Literature, and Language Education· 0 citations
Abstract
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.
Generative AI (GenAI) has transformed L2 writing, producing human-like prose but often impersonal feedback. This study explores the potential of GenAI–human collaborative feedback, focusing on the first author’s experience as a teaching assistant in a Hong Kong public university’s Bachelor of Education (English language track). Grounded in Ecological Languaging Competencies (ELC) and its affordance framework, this study employs an ethnographic approach informed by narrative inquiry and phenomenology. Data were drawn from Zoom tutoring sessions incorporating interview-style questions to investigate participants’ perspectives and experiences, GenAI-student conversation logs, and final assignments in order to analyze two multilingual students’ GenAI–human-mediated L2 writing processes. Findings are organized around three ELC-informed themes: (1) whole-body sense-making and the meshing of first-order languaging and second-order language; (2) individual languaging agency within a distributed ecosystem; and (3) environmental affordances and functional fit. In both cases, GenAI demonstrates consistent limitations in facilitating the situated, embodied, and affectively attuned dimensions of languaging that effective L2 writing entails. This study makes two contributions: it extends ELC’s affordance network to tertiary-level GenAI–human-mediated L2 writing, and it reconceptualizes writerly authorship as a distributed yet agentively orchestrated practice. Moreover, co-agentic GenAI–human feedback foregrounds ecological embeddedness, writerly agency, and ethical GenAI integration.
Luan Xi, Qinghua Chen, A. M. Lin· Education sciences· 0 citations
Recent research has increasingly focused on the application of generative artificial intelligence (genAI) in English as a Foreign Language (EFL) writing instruction, prompted by rapid technological advancements and growing pedagogical interest. Despite this trend, systematic reviews have not yet examined how learners are conceptualized within this field. To address this gap, the present study systematically reviewed 17 empirical studies, selected from an initial pool of 316, in accordance with PRISMA guidelines. The analysis identified three primary conceptualizations of writing: accuracy-oriented, process-oriented, and generative, each linked to distinct learner roles that range from active participants to more passive or dependent users. The findings revealed that learner agency is influenced more by pedagogical integration than by the technology itself. Structured uses of AI promoted engagement and higher-order thinking, whereas generative uses may lead to over-reliance and partial delegation of authorship. This review underlined the importance of teaching EFL writing through theoretically informed, process-oriented approaches that empower learners and incorporate effective AI use.
Hani Hamad, M. Albelihi, M. Rice et al.· British Journal of Applied L...· 0 citations
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.
Yang Jiao, Jing Huang· Region - Educational Researc...· 0 citations
Generative artificial intelligence (GenAI) is increasingly used to support EFL writing and speaking, yet research has focused more heavily on written products than on the combined processes of oral and written production, authorship, and teacher mediation. This qualitative study explored how students and teachers in a Colombian university language program perceived GenAI-supported production. Participants were seven students who completed an open-ended semi-structured questionnaire and five teachers who participated in semi-structured interviews. Data were examined through iterative thematic coding, analytic memoing, and comparison across participant groups. Four patterns emerged: GenAI as a rehearsal and revision partner; tension between polished output and language ownership; teacher mediation shifting toward critical language awareness; and the need for explicit ethical and assessment guidance. The study highlights that GenAI is pedagogically valuable when transparent, process-oriented, and dialogic routines preserve learner agency, human feedback, and responsible authorship.
Dionelio Jesus Moreno Villalobos· International Journal of AI...· 0 citations
Generative artificial intelligence has created new possibilities for supporting second language academic writing, but less is known about how learners take up such support within ordinary writing instruction. Drawing on Activity Theory, this qualitative classroom-based case study examined how ChatGPT was used in an Uzbek university academic writing course and what tensions shaped students' movement from AI assistance toward appropriation. The participants were 10 first-year EFL students majoring in English literature and English language teaching at Fergana State University. Across a ten-week course, data were collected through a background questionnaire, baseline writing task, six AI-mediated writing tasks, ChatGPT interaction logs, draft-revision pairs, reflective writing logs, semi-structured interviews, stimulated recall interviews, and teacher field notes. Thematic analysis identified three themes. First, students initially used ChatGPT as a substitute for difficult writing work, especially for paragraph generation, correction, and rewriting, but gradually developed more task-specific uses. Second, ChatGPT created a tension between convenience and learning responsibility, as it helped students produce more fluent paragraphs while sometimes weakening control over meaning, authorship, and revision decisions. Third, teacher mediation, including AI-use rules, prompt modeling, feedback, reflection, and classroom dialogue, helped regulate AI use. The findings suggest that ChatGPT-mediated writing development depends not only on tool access but also on pedagogical mediation that helps learners evaluate, regulate, and explain AI-supported writing decisions.
Ismail Xodabande, Abdulxay Qosimov, Kh.F. Umarova· Technology in Language Teach...· 2 citations
This review investigates how recent studies conceptualise the theoretical foundations, pedagogical practices, learning outcomes, and the integration of technology and artificial intelligence within genre-based writing instruction, and proposes an integrated framework that links the genre-based approach with emerging AI-supported writing practices.
Zirui Chen, Nazeera Ahmed Bazari, G. Narayanan· Arab World English Journal· 0 citations