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arXiv · 2601.17000

Investigating Self-regulated Learning Sequences within a Generative AI-based Intelligent Tutoring System

Jie Gao·Shasha Li·Jianhua Zhang·Shan Li·Tingting Wang
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Abstract

There has been a growing trend in employing generative artificial intelligence (GenAI) techniques to support learning. Moreover, scholars have reached a consensus on the critical role of self-regulated learning (SRL) in ensuring learning effectiveness within GenAI-assisted learning environments, making it essential to capture students' dynamic SRL patterns. In this study, we extracted students' interaction patterns with GenAI from trace data as they completed a problem-solving task within a GenAI-assisted intelligent tutoring system. Students' purpose of using GenAI was also analyzed from the perspective of information processing, i.e., information acquisition and information transformation. Using sequential and clustering analysis, this study classified participants into two groups based on their SRL sequences. These two groups differed in the frequency and temporal characteristics of GenAI use. In addition, most students used GenAI for information acquisition rather than information transformation, while the correlation between the purpose of using GenAI and learning performance was not statistically significant. Our findings inform both pedagogical design and the development of GenAI-assisted learning environments.

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Jie Gao, Shasha Li, Jianhua Zhang, Shan Li, Tingting Wang. 2026-01-13. Investigating Self-regulated Learning Sequences within a Generative AI-based Intelligent Tutoring System. https://arxiv.org/abs/2601.17000

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