This presentation examines a multimodal, project-based “Manga” creation task to support Japanese EFL novices’ source-based writing. It shows students paraphrased, summarized, and transformed academic texts into colloquial dialogue while experiencing challenges in maintaining rhetorical consistency. Results indicate the potential of this approach to develop discourse synthesis skills in L2 writing.
This autoethnography explores the conflict between Generative Ais’ standardized feedback and an L2 writer’s intentional stylistic rhetorical design. By analyzing negotiation logs, I illustrate how AIs prioritize pseudo-authoritative, opaque norms over strategic style choices. The findings suggest that AI-driven corrections risk colonizing the writer’s voice and stripping away authorial ownership.
This study examines sentence structure in multilingual writing among Polish L1, English L2, and Swedish L3 university students. Findings reveal that sentence patterns result from interactions of language-specific norms, cross-linguistic transfer, individual writing style, and proficiency. Higher L3 proficiency promotes target-language conformity, while personal stylistic traits persist across languages.
This session examines how AI shapes linguistic diversity, access, and standards in writing. Drawing on literacy and L2 writing scholarship, it explores human dimensions of language, identity, and pedagogy, highlighting AI’s impact on reinforcing dominant linguistic norms.
This study examines how AI integration influences IELTS writing teachers’ professional identity negotiation. Using collaborative reflection panels and narrative frames, findings suggest that AI reshapes teachers’ identities by expanding their role as critical mediators, reaffirming human values, transforming anxiety as a catalyst for professional growth, and reinforcing authoritative human expertise.
This presentation introduces a Quantum Dialogic framework for L2 writing that centers human agency in AI-mediated classrooms. Drawing on agential realism, it shows how dialogic peer review, narrative grounding, and reflective metacognition help teachers guide ethical decision-making, preserve student voice, and sustain meaningful human interaction in the age of AI.
This study explores how multilingual TESOL graduate students negotiate authorial voice in AI-assisted academic writing. Using Kong and Zhang’s (2021) cognitive, affective, and sociocultural AI literacy framework, it examines strategies, emotions, and contextual influences, highlighting how writers critically engage with AI while preserving voice and identity, offering pedagogical insights.
