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.

翻译：尽管AI工具日益嵌入学术实践，但关于大学生如何将其融入写作过程的研究尚不充分。本研究考察学生在不同写作任务中与AI的互动方式，以及这种互动如何受到包括AI素养、写作信心、信任度、作者身份关切及动机在内的个体因素影响。研究1对107名英国大学生进行问卷调查，绘制了五个写作阶段（构思、溯源、规划、草拟、修订）中AI使用的任务特定模式与共现模式，并分析其与个体因素的关联。研究2通过访谈12名研究生（反思其在课程评估写作中对AI的既成使用习惯），补充探究这些模式如何在实践中被整合。两项研究共同表明，AI整合具有选择性与异质性，形成了三种反复出现的价值导向配置：（1）前期导向（学习型）——工具支持探索与理解；（2）后期导向（质量型）——工具支持起草与润色；（3）外围导向（效率型）——工具用于减少摩擦并维持写作进程的连贯性。我们提出了AI辅助学术写作的工作流层级模型，揭示学生如何在学习、质量、效率与作者身份这些竞争性优先事项之间进行权衡，以及他们如何评估并承担AI生成内容的责任。