# Evidence-centered Assessment for Writing with Generative AI

arXiv: https://arxiv.org/abs/2401.08964 · HTML: https://mlsanity.com/paper/2401.08964/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2024.

## Built on by (most cited first, 4 of 4)

- [Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process](https://mlsanity.com/paper/2504.12488/) (2025)
- [Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality](https://mlsanity.com/paper/2412.07200/) (2024)
- [When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration](https://mlsanity.com/paper/2607.15006/) (2026)
- [Positioning Generative Artificial Intelligence in STEM Assessment: When to Require, Scaffold, or Restrict Its Use](https://mlsanity.com/paper/2608.07475/) (2026)

## Abstract (arXiv)

We propose a learning analytics-based methodology for assessing the collaborative writing of humans and generative artificial intelligence. Framed by the evidence-centered design, we used elements of knowledge-telling, knowledge transformation, and cognitive presence to identify assessment claims; we used data collected from the CoAuthor writing tool as potential evidence for these claims; and we used epistemic network analysis to make inferences from the data about the claims. Our findings revealed significant differences in the writing processes of different groups of CoAuthor users, suggesting that our method is a plausible approach to assessing human-AI collaborative writing.