FROM PRODUCT TO PROCESS: EXPLORING GENERATIVE ARTIFICIAL INTELLIGENCE INTEGRATION IN L2 STUDENTS’ DIGITAL MULTIMODAL COMPOSITION
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Abstract
The unique GenAI affordances for generating multiple modes have encouraged its integration into digital multimodal composition (DMC) production, with potential implications for product quality. The study aims to analyze L2 students’ DMC products in three distinct courses and explore GenAI integration during the composition processes. In this descriptive qualitative study, 63 DMC products were analyzed using Hafner and Ho’s (2020) process-oriented DMC assessment rubric, and the integration was explored through in-depth interviews involving 10 purposively selected designers and through documentation. The findings reveal moderate-to-high quality, ranging from a mean score of 3.27 to 4.38 across classes. The strongest performance was observed in the language dimension, with greater variability in multimedia-visual and organization-content. By course, the DMC products in Academic Writing were rated the most qualified, albeit with the highest variability, compared to those in English for Young Learners, which demonstrated more consistent quality ratings. Throughout the process, most participants critically engaged with GenAI in dynamic and iterative ways, particularly at the idea generation, referencing, editing, and design stages, displaying digital student agency. This study contributes to the role of GenAI in DMC production and how the process may potentially shape the quality and student agency, providing theoretical and pedagogical implications for relevant parties.
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