Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality
Kaixun Yang, Mladen Rakovi\'c, Zhiping Liang, Lixiang Yan, Zijie Zeng,, Yizhou Fan, Dragan Ga\v{s}evi\'c, Guanliang Chen

TL;DR
This study investigates how different levels of student engagement with Generative AI during writing influence essay quality, revealing that active modification improves outcomes while passive acceptance may hinder quality.
Contribution
It introduces a novel causal analysis of GAI-assisted writing behaviors and their impact on essay quality using the X-Learner method on a large dataset.
Findings
Active engagement through modification enhances lexical and syntactic quality.
Passive acceptance of GAI suggestions may decrease essay quality.
Using GAI can help reduce linguistic bias in student writing.
Abstract
Students are increasingly relying on Generative AI (GAI) to support their writing-a key pedagogical practice in education. In GAI-assisted writing, students can delegate core cognitive tasks (e.g., generating ideas and turning them into sentences) to GAI while still producing high-quality essays. This creates new challenges for teachers in assessing and supporting student learning, as they often lack insight into whether students are engaging in meaningful cognitive processes during writing or how much of the essay's quality can be attributed to those processes. This study aimed to help teachers better assess and support student learning in GAI-assisted writing by examining how different writing behaviors, especially those indicative of meaningful learning versus those that are not, impact essay quality. Using a dataset of 1,445 GAI-assisted writing sessions, we applied the cutting-edge…
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Taxonomy
TopicsOnline Learning and Analytics
