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

arXiv: https://arxiv.org/abs/2604.25389 · HTML: https://mlsanity.com/paper/2604.25389/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 17 of 17)

- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation](https://mlsanity.com/paper/2310.12953/) (2023)
- [Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems](https://mlsanity.com/paper/2204.07666/) (2022)
- [Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models](https://mlsanity.com/paper/2402.11723/) (2024)
- [Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation](https://mlsanity.com/paper/2401.10838/) (2024)
- [ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models](https://mlsanity.com/paper/2310.00117/) (2023)
- [From Pen to Prompt: How Creative Writers Integrate AI into their Writing Practice](https://mlsanity.com/paper/2411.03137/) (2024)
- ["It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models](https://mlsanity.com/paper/2411.13032/) (2024)
- [The AI Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment](https://mlsanity.com/paper/2312.07086/) (2023)
- [Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction](https://mlsanity.com/paper/2409.16708/) (2024)
- [Untangling Critical Interaction with AI in Students Written Assessment](https://mlsanity.com/paper/2404.06955/) (2024)
- [Collaborative Document Editing with Multiple Users and AI Agents](https://mlsanity.com/paper/2509.11826/) (2025)
- [Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument](https://mlsanity.com/paper/2305.01186/) (2023)
- [AI Rivalry as a Craft: How Resisting and Embracing Generative AI Reshape Writing Professions](https://mlsanity.com/paper/2503.09901/) (2025)
- [The AI Memory Gap: Users Misremember What They Created With AI or Without](https://mlsanity.com/paper/2509.11851/) (2025)
- ["If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace](https://mlsanity.com/paper/2602.01386/) (2026)

## Abstract (arXiv)

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.