Doing DDL with AI: Classroom Interactions in Collaborative Writing

Abstract

Abstract In the last few decades, data-driven learning and generative artificial intelligence (AI) applications have become increasingly visible in academic writing instruction. The data-driven learning approach promotes students’ lexical and morphological awareness by encouraging them to explore language in context, while generative AI tools offer new possibilities for idea generation, text development, and linguistic feedback. However, limited process-based studies exist on how these two approaches work together in collaborative writing in the classroom and how teacher guidance shapes this process. This study aims to explore how B2-level students enrolled in a university preparatory program use data-driven learning and generative AI tools in the collaborative opinion essay writing process. Designed as a qualitative classroom-based case study, the research was conducted over a seven-week module. Structured student diaries, teacher journals and semi-structured interviews were used in the data collection process. Findings indicate that tool use was constructed through peer negotiation and teacher mediation. Students used generative AI for idea generation and corpus tools for lexical verification and contextual checking. The study highlights the critical role of pedagogical design and teacher facilitation in DDL-AI-assisted writing instruction.