Aug 2026· Review of Artificial Intelligence in Education· Vol 7, pp. e01395· 0 citations· 12 references
TL;DR
The findings suggest that Generative AI can become a substantive curriculum-design resource without displacing educators’ role as designers and reinforce the need for continued professional judgment in Generative AI-supported curriculum design.
Abstract
Objective: This study examines how the linguistic choices made by a human curriculum designer and Google’s NotebookLM constructed their roles and relationship during their collaborative curriculum design, and what these patterns revealed about the distribution and mobilization of human and technological resources.
Methods: This work adopts an autoethnographic design and analyzes a two-month written interaction between an experienced curriculum designer and NotebookLM during the redesign of an undergraduate education class in the UK. The corpus comprised 367 interactional turns and 50,832 words. The interaction was analyzed discursively through Systemic Functional Linguistics and Appraisal Theory. Brown’s (2011) Design Capacity for Enactment framework was subsequently employed to interpret the 48 curriculum-design episodes identified in the corpus.
Results: The interaction was collaborative and increasingly personalized, but asymmetrical in design authority. NotebookLM contributed extensive textual production, elaboration, source synthesis, and strongly affirmative language, while the Human Designer expressed pedagogical intentions, evaluated proposals, controlled sequencing, and determined what should be accepted, modified, or rejected. The design episodes showed that adaptation was the most common form of resource appropriation, while instances of rejection provided additional evidence of human design authority.
Discussion: The findings suggest that Generative AI can become a substantive curriculum-design resource without displacing educators’ role as designers. NotebookLM expanded the range of candidate curricular representations, but their relevance depended on human evaluation, contextualization, and transformation. Linguistic alignment and extensive AI production did not necessarily correspond with design authority or successful implementation, reinforcing the need for continued professional judgment in Generative AI-supported curriculum design.
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
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
Digital reading takes place in environments that organize content, compete for attention, and shape readers’ pathways; therefore, the expansion of generative artificial intelligence makes the distance between mastering a tool and critically engaging with what it provides more visible. This article analyzes how students constitute themselves as reader-subjects in digital practices and discusses their possibilities for participation in relation to automated systems, although artificial intelligence was not part of the original corpus. The qualitative-dialogical study was designed as action research, draws on Lüdke and André (1986), and involved 46 first-year students enrolled in integrated technical secondary education at the Goiânia Campus of the Federal Institute of Goiás. The corpus comprises an online questionnaire, interviews conducted during synchronous meetings, field diary records, and digital productions. The analysis mobilizes the notions of subject, discursive formation, discursive memory, and interdiscourse, based on Pêcheux (1990) and Orlandi (1999, 2001, 2003), together with dialogism, utterance, verbal interaction, and responsivity, discussed by Bakhtin (1992) and Bakhtin and Volochínov (2006). The statements associate digital environments with speed, access, and learning, but also with fatigue, distraction, and difficulty separating study from rest, showing that readers’ choices are constituted through relations with peers, teachers, institutions, and media. The study argues for source evaluation, textual revision, and justification of choices, while raising questions about authorship and critical reading in AI-mediated school activities.
Limerce Ferreira Lopes· Revista de Estudos Interdisc...· 0 citations
The rapid emergence of generative artificial intelligence (AI) has challenged established assumptions about authorship, learning and knowledge production in higher education. Recent research in higher education has focused on AI as a pedagogical tool enhancing student engagement, presenting institutional challenges or raising ethical concerns. However, far less attention has been given to the methodological implications of researching with generative AI. This article experiments with conversational intra-drama as a posthumanist approach to qualitative inquiry.
Drawing on posthumanist theory, particularly Barad's concept of intra-action, the study engages ChatGPT in a conversational exchange with the researcher. Rather than treating dialogues as supplementary material, the conversation is positioned as the central site of inquiry. Through a conversational format resembling a semi-structured interview, the human–AI dialogue is analysed diffractively to explore how meaning emerges through entangled interactions between human and non-human participants.
A diffractive reading of the conversational exchanges reveals tensions surrounding epistemic authority, academic integrity and the evolving role of AI in higher education. These reflections emerge through the intra-actions between human prompts and algorithmically generated responses, illustrating how knowledge unfolds relationally within human–AI dialogue rather than through fixed analytical conclusions.
The article contributes to methodological debates in qualitative research by proposing conversational intra-drama as a posthumanist approach for researching human–AI dialogue. By positioning generative AI as a more-than-human conversational participant, the study invites qualitative researchers to think with non-human actors in the production of knowledge, extending post-qualitative approaches to digitally mediated research environments.
Wedsha Appadoo-Ramsamy· Qualitative Research Journal· 0 citations
This study proposes a methodological approach in which large language models (LLMs) serve as reflective and dialogical partners rather than analysts in critical autoethnographic narrative (CAN) research on language teacher educator identity. During an ongoing debate about the use of generative artificial intelligence (AI) in reflexive qualitative research, this approach occupies a narrow methodological space. LLM outputs serve as stimuli for the researcher's reflexive interpretation of CAN texts, while meaning‐making, salience judgment, and theoretical synthesis remain human practices. In a two‐phase design, three LLMs (ChatGPT 5.2 Thinking, Claude 4.5 Sonnet, and Gemini 3 Thinking) first generated reflective stimuli in the form of probing questions and then produced parallel thematic outputs that served only as material for the researcher's reflexive synthesis, not as completed analyses. Four patterns emerged: consciousness‐raising through dialogical awakening, institutional precarity and resistance, pedagogical transformation, and the relational construction of critical identity. The LLMs differed in orientation in ways consistent with poststructuralist epistemology. The contribution is primarily methodological: a procedure for AI‐stimulated reflexive practice in language teacher education research, alongside a critical engagement with the ethical, environmental, and epistemic costs such practice entails.
This article presents a creative writing course incorporating AI, taught at Université Lumière Lyon 2 (France) in September–December 2024. The course combined exploration of LLMs with critical reflection on their use. It drew inspiration from
Si Rome n’avait pas chuté
by Raphaël Doan (2023), written with ChatGPT 3 and 3.5, which imagines a world where industrialisation began under Nero. Students used ChatGPT to write counterfactual stories or fan fictions: these literary genres are especially useful for creating a three-way relationship between the human writer, the LLM, and an external referent that drives the interaction between the two. The pedagogical progression was structured around three phases: idea generation, narrative structuration, and writing. I generated a prompt live in class: ‘What if Medea had killed Jason instead of her children?’ The students conceived extended chains of prompts to guide the model’s responses and refine narrative variations. Some engaged with contemporary novels, others reimagined Graeco-Roman myths, which will be the scope of this article. Mythological rewritings prove particularly rich for AI-assisted storytelling, revitalising courses on classical myths in a playful, dialectical, and interactive manner. They raise a wide range of ethical issues, especially regarding the sources from which LLMs draw and the content policies that lead to the suppression of certain aspects of ancient literature. Asking ChatGPT to rework classical myths also reveals its tendency to homogenise narratives. This highlights the ongoing need for human oversight: when approached critically, generative AI can provide an effective introduction to classical mythology.
Valentin Decloquement· The Journal of Classics Teac...· 0 citations