Co-Writing with AI: An Empirical Study of Diverse Academic Writing Workflows

TL;DR

This study investigates how university students incorporate AI tools into various stages of academic writing, revealing diverse, task-specific, and value-driven usage patterns influenced by individual factors.

Contribution

It provides an empirical analysis of students' AI integration in academic writing workflows, highlighting three main configurations and their underlying motivations.

Findings

AI use is selective and varies across writing stages.

Three recurring AI usage configurations identified: learning-oriented, quality-oriented, productivity-oriented.

Students evaluate and take responsibility for AI-generated outputs.

Abstract

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.