Beyond Self-Regulated Learning Processes: Unveiling Hidden Tactics in Generative AI-Assisted Writing

TL;DR

This paper models the complex, layered nature of self-regulated learning in students using GenAI tools, revealing distinct strategies and their impact on academic performance through Hidden Markov Models.

Contribution

It introduces a layered SRL model and applies Hidden Markov Models to analyze non-linear, recursive SRL behaviors in GenAI-assisted writing contexts.

Findings

Identified three distinct SRL strategy groups

Different SRL strategies correlate with varied performance outcomes

Proposed a new layered SRL conceptualization for dynamic learning analysis

Abstract

The integration of Generative AI (GenAI) into education is reshaping how students learn, making self-regulated learning (SRL) - the ability to plan, monitor, and adapt one's learning - more important than ever. To support learners in these new contexts, it is essential to understand how SRL unfolds during interaction with GenAI tools. Learning analytics offers powerful techniques for analyzing digital trace data to infer SRL behaviors. However, existing approaches often assume SRL processes are linear, segmented, and non-overlapping-assumptions that overlook the dynamic, recursive, and non-linear nature of real-world learning. We address this by conceptualizing SRL as a layered system: observable learning patterns reflect hidden tactics (short, purposeful action states), which combine into broader SRL strategies. Using Hidden Markov Models (HMMs), we analyzed trace data from higher education students engaged in GenAI-assisted academic writing. We identified three distinct groups of learners, each characterized by different SRL strategies. These groups showed significant differences in performance, indicating that students' use of different SRL strategies in GenAI-assisted writing led to varying task outcomes. Our findings advance the methodological toolkit for modeling SRL and inform the design of adaptive learning technologies that more effectively support learners in GenAI-enhanced educational environments.