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This paper investigates how university students integrate AI tools into their academic writing workflows across different stages and tasks. Through a survey of 107 students and interviews with 12 postgraduates, the study identifies three recurring configurations of AI use: early-stage (learning-oriented), late-stage (quality-oriented), and peripheral (productivity-oriented). The findings highlight the selective and heterogeneous nature of AI integration, revealing how students balance learning, quality, productivity, and authorship concerns.
Students aren't blindly adopting AI for writing; they're strategically weaving it into specific workflows to boost learning, polish drafts, or overcome friction, revealing nuanced value-driven configurations.
Despite AI tools becoming increasingly embedded in academic practice, little is known about how university students integrate them into their writing processes. We examine how students engage with AI across different writing tasks, and how this engagement is shaped by individual factors including AI literacy, writing confidence, trust, authorship concerns, and motivation. Study~1 surveys 107 UK university students to map task-specific and co-occurring patterns of AI use across five writing stages (ideation, sourcing, planning, drafting, and reviewing) and their associations with individual factors. Study~2 complements this by exploring how these patterns can be assembled in practice, through interviews with 12 postgraduates reflecting on their established use of AI in assessed writing. Together, the studies suggest that AI integration is selective and heterogeneous, forming three recurring and value-oriented configurations: (1) early-stage (learning-oriented), where tools support exploration and understanding; (2) late-stage (quality-oriented), where tools support drafting and refinement; and (3) peripheral (productivity-oriented), where tools are used to reduce friction and sustain momentum across the process. We offer a workflow-level account of AI-supported academic writing, showing how students navigate competing priorities of learning, quality, productivity, and authorship, and how they evaluate and take responsibility for AI-generated outputs.
