Date
Publisher
arXiv
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.
Study at a glance
- Question: How do students regulate and manage their own learning when writing with generative AI?
- Setting and data: Digital trace data from higher education students engaged in generative AI-assisted academic writing.
- Method: Statistical modeling of digital trace data (Hidden Markov Models)
Why it matters
As generative AI reshapes how students learn, understanding self-regulated learning with AI tools is essential. However, existing approaches assume learning processes are linear and segmented, overlooking the dynamic, recursive, and non-linear nature of real-world learning.
Key findings
- In higher education academic writing, students using generative AI fell into three distinct groups with significantly different task performance.
- Students' use of different self-regulation strategies while writing with generative AI was linked to their varying task outcomes.
- Students' observable learning behaviors reflected underlying, purposeful action tactics that combined into broader self-regulation strategies.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Study design
