Ink and Algorithm: Exploring Temporal Dynamics in Human-AI Collaborative Writing

TL;DR

This study investigates how humans and generative AI collaborate in writing, identifying temporal usage patterns and their impact on human cognitive behaviors to improve educational support tools.

Contribution

It introduces a novel analysis of temporal AI usage patterns and their correlation with human writing behaviors in collaborative writing, advancing understanding of human-AI interaction.

Findings

Four major AI usage patterns identified

Significant correlations between AI patterns and human cognitive behaviors

Insights for designing better human-AI collaborative writing tools

Abstract

The advent of Generative Artificial Intelligence (GAI) has revolutionized the field of writing, marking a shift towards human-AI collaborative writing in education. However, the dynamics of human-AI interaction in the collaborative writing process are not well understood, and thus it remains largely unknown how human learning can be effectively supported with such cutting-edge GAI technologies. In this study, we aim to bridge this gap by investigating how humans employ GAI in collaborative writing and examining the interplay between the patterns of GAI usage and human writing behaviors. Considering the potential varying degrees to which people rely on GAI usage, we proposed to use Dynamic Time Warping time-series clustering for the identification and analysis of common temporal patterns in AI usage during the human-AI collaborative writing processes. Additionally, we incorporated Epistemic Network Analysis to reveal the correlation between GAI usage and human writing behaviors that reflect cognitive processes (i.e., knowledge telling, knowledge transformation, and cognitive presence), aiming to offer insights for developing better approaches and tools to support human to learn effectively via such human-AI collaborative writing activities. Our findings reveal four major distinct temporal patterns in AI utilization and highlight significant correlations between these patterns and human writing behaviors. These findings have significant implications for effectively supporting human learning with GAI in educational writing tasks.