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        <datestamp>2026-09-30T00:34:52Z</datestamp>
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          <dc:title>Xuehua Fu: BEYOND THE ESSAY: What happens when language learners compose with words, images, video and GenAI?</dc:title>
          <dc:creator>Xuehua Fu (16922625)</dc:creator>
          <dc:subject>Curriculum and pedagogy theory and development</dc:subject>
          <dc:subject>writing</dc:subject>
          <dc:subject>digital multimodal composition</dc:subject>
          <dc:subject>generative AI-mediated composition</dc:subject>
          <dc:subject>foreign language learning</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;Against the digital and multimodal turn, this mixed-methods quasi-experimental study proposed a Generative AI-assisted process-multimodal genre (GenAI-P-MG) framework and examined its effects on digital multimodal composing (DMC) and linguistically oriented English writing. Fifty-five English-major undergraduates at a Chinese university participated in a 10-week intervention. Data included pre- and post-writing tests, video projects, composing-process screen recordings, video-stimulated recall interviews, and semi-structured interviews. Findings showed that GenAI-P-MG-embedded writing instruction fostered more systematic preparation, global evaluation, and broader human-GenAI collaboration. The experimental group produced higher-quality DMC products overall and in four sub-dimensions, while developing stronger designer agency and greater audience, procedural, and task-complexity awareness. Effects on conventional writing were selective and dimension-specific, with advantages in syntactic complexity, comprehensibility, and qualitative developmental experiences, but weaker, less favourable, or conditional effects in other dimensions. The study offers theoretical, methodological, and pedagogical implications for integrating GenAI-assisted DMC into EFL writing classrooms.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-30T00:34:52Z</dc:date>
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          <dc:identifier>10.17608/k6.auckland.34004562.v2</dc:identifier>
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