Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality
Open accessAbstract
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 method, X-Learner, to quantify the causal impact of three GAI-assisted writing behavioral patterns (i.e., seeking suggestions but not accepting them, seeking suggestions and accepting them as they are, and seeking suggestions and accepting them with modification) on four measures of essay quality (i.e., lexical sophistication, syntactic complexity, text cohesion, and linguistic bias). Our analysis showed that writers who frequently modified GAI-generated text-suggesting active engagement in higher-order cognitive processes-consistently improved the quality of their essays in terms of lexical sophistication, syntactic complexity, and text cohesion. In contrast, those who often accepted GAI-generated text without changes, primarily engaging in lower-order processes, saw a decrease in essay quality. Additionally, while human writers tend to introduce linguistic bias when writing independently, incorporating GAI-generated text-even without modification-can help mitigate this bias.
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The quality of GenAI output has been shown to be enhanced through an iterative interaction with the user.
“In terms of professional output, GenAI has been found to produce good quality academic texts but can also produce ‘hallucinations’ of entirely fictitious ideas pre sented as facts (Babl & Babl, 2023 who asked the GenAI to write aca demic abstracts), although the quality of the output has been shown to be enhanced through an iterative interaction with the user (Yang et al., 2025, Cheng et al., 2”
From If ChatGPT can do it, where is my creativity? generative AI boosts performance but diminishes experience in creative writing (2025) · cites this paper as Yang et al., 2025
The Results (p. 10) and Discussion (p. 13) find that writers who revise GenAI suggestions produce higher-quality essays than those who accept suggestions unchanged. The study measures the quality of the finished, human-revised writing; it does not show that iterative interaction improves the quality of GenAI's own output. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrevious studies on GAI-assisted writing often categorize writers based on overall AI usage throughout the entire writing session, overlooking temporal dynamics.
“However, these existing studies often have two primary shortcomings: (i) They generally categorized writers based on their overall AI usage throughout the entire writing session, overlooking the temporal dynamics of these behaviours as the progression of a writing session (studies by Cheng et al., 2024;Shibani et al., 2023;Wambsganss et al., 2023;Yang et al., 2025);”
From Ink and algorithm: Exploring temporal dynamics in generative <scp>AI</scp> ‐assisted writing (2026) · cites this paper as Yang et al., 2025
Section 3.2 (p. 6) classifies writers in this study by the overall frequency of AI-assisted behavior patterns, supporting that part of the claim. But Section 2.2 (p. 5) describes an earlier study that used time-series clustering, so this paper does not support treating the neglect of temporal dynamics as a general shortcoming of previous studies. (LLM, conf=0.83)
Experimental — accuracy not yet measuredYang et al. (2025) demonstrated that writers who actively engaged in higher order cognitive processes consistently produced higher-quality essays.
“Similarly, Yang et al. (2025) demonstrated that writers who actively engaged in higher order cognitive processes consistently produced higher-quality essays.”
From Ink and algorithm: Exploring temporal dynamics in generative <scp>AI</scp> ‐assisted writing (2026) · cites this paper as Yang et al., 2025
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- Authors
- Yang, K., Raković, M., Liang, Z., Yan, L., Zeng, Z., Fan, Y., Gašević, D., & Chen, G.
- Affiliations
- Monash University (AU)
- Peking University (CN)
- ORCID
- Venue
- Proceedings of the 15th International Learning Analytics and Knowledge Conference
- Publisher
- ACM
- Published
- 2025-03-03
- Pages
- 568-578
- OpenAlex
- W4407842322
- Cited by
- 26
- References
- 55
- Citations by year
- 2026: 11 · 2025: 15
- FWCI
- 29.73
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion, cc-by)
- Monash University Research Portal (Monash University) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Research Council (DP240100069, DP220101209)
- Jacobs Foundation (CELLA 2 CERES)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Writing and Handwriting Education
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Keywords
- essay quality, cognitive engagement, lexical sophistication, syntactic complexity, textual cohesion, language bias, writing assessment, higher-order cognition
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