# DraftTrace: A Multi-View Analytics Environment for AI-Integrated Writing

arXiv: https://arxiv.org/abs/2609.36544 · HTML: https://mlsanity.com/paper/2609.36544/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 3 of 3)

- [Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality](https://mlsanity.com/paper/2412.07200/) (2024)
- [Characterizing Students' LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks](https://mlsanity.com/paper/2605.04534/) (2026)
- [Effects of Varying LLM Access on Essay Writing Behavior](https://mlsanity.com/paper/2606.00250/) (2026)

## Abstract (arXiv)

Generative AI has changed how students produce writing assignments. The final artifact is no longer sufficient to understand the process through which it was produced. We introduce DraftTrace, a writing environment that jointly captures three complementary views of writing: the final product, the writing process and interactions with an integrated AI-assistant. DraftTrace reconstructs how a document develops over time and organizes these signals into submission, longitudinal, and class-level analytics for instructors. We deployed DraftTrace in a graduate NLP course with 81 students and compared their sessions with LLM-generated responses entered by automated tools and with copy-typed responses. While product measures distinguish differences in text formulation, process measures distinguish differences in how text is entered. Considering both views together helps characterize cases such as copy-typing. Interaction traces show that students use the assistant differently across stages of writing: to clarify the question at an early stage and to verify answers at a later stage. A preliminary instructor survey highlights the importance of multi-view writing analytics and their interpretability.