Ink and algorithm: Exploring temporal dynamics in generative AI ‐assisted writing

The advent of Generative AI (GAI) has transformed writing, marking a shift towards GAI‐assisted writing in education. However, the dynamics of human–AI interaction in the writing process are not well‐understood, and thus, it remains largely unknown how human learning can be effectively supported with such technologies. This study addresses this gap by investigating how humans employ GAI during writing and examining the interplay between patterns of GAI usage and writing behaviours. To capture these patterns, we applied Dynamic Time Warping time‐series clustering to identify temporal trajectories of GAI use and employed Epistemic Network Analysis to examine how these trajectories relate to cognitive processes such as knowledge telling, knowledge transformation and cognitive presence. Our analysis revealed four distinct temporal patterns of GAI usage (ie, AI‐critical writers, AI‐dependent writers, AI‐independent writers and AI‐balanced writers), each associated with different cognitive engagement strategies. The findings suggest that some writers tend to rely excessively on GAI, which may limit opportunities for meaningful learning by reinforcing surface‐level strategies such as knowledge telling. These insights highlight the need for researchers and educators to design GAI‐based writing assistants that are sensitive to students' temporal dynamics and can scaffold more productive cognitive engagement. Practitioner notes What is already known about this topic Researchers and educators can gain practical insights into achieving intelligence augmentation through critical engagement by studying effective user behaviours for enhanced human–AI partnership in writing. Generative AI‐assisted writing can be evaluated using an evidence‐centred assessment framework, which relates to writing cognitive processes. Current studies on Generative AI‐assisted writing usually classify writers according to their overall AI usage behaviours throughout the entire writing session. What this paper adds We propose using time‐series clustering to identify and analyse common temporal patterns in AI usage during generative AI‐assisted writing processes. We uncover the correlation between temporal patterns in AI usage and human writing behaviours, which reflect cognitive processes, through Epistemic Network Analysis. We identify four major distinct temporal patterns in AI utilization and highlight that each pattern is correlated with different cognitive processes. Implications for practice and/or policy Researchers and educators should be aware of the risks associated with students overly relying on GAI for writing tasks, as this dependency can hinder opportunities for meaningful learning. Researchers and educators should carefully consider the types of suggestions provided by GAI‐based writing tools when integrating them into educational contexts.