Integrating generative AI into digital multimodal composition: A study of multicultural second-language classrooms

Abstract

This study examines the integration of generative AI tools into digital multimodal composition (DMC) within a multicultural context, examining their impact on students’ motivation, writing processes, and outcomes. Eleven culturally diverse students from two high schools in Hong Kong participated in the study. The study developed and employed a novel pedagogical framework, IDEA (Interpret, Design, Evaluate, and Articulate), to seamlessly incorporate generative AI into DMC practices. Data-collection methods included analysis of generative AI tool-usage history, classroom video observations, surveys, and interviews. The findings reveal that students leveraged generative AI's capabilities across five key areas: content generation, feedback and revision, multilingual support, critical thinking, and visual representation. The integration of AI tools followed distinct stages in the composition process, resulting in enhancements to the vocabulary, grammar, and structural elements of students’ work. This research contributes to the growing body of knowledge on the intersection of generative AI, education, and multimodal literacy, with a particular emphasis on human-AI collaboration in multicultural settings. It also offers valuable insights for educators seeking to enhance students’ DMC skills through the thoughtful integration of generative AI tools, potentially increasing engagement, motivation, and creative expression among learners from diverse cultural backgrounds.