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

arXiv: https://arxiv.org/abs/2601.17000 · HTML: https://mlsanity.com/paper/2601.17000/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [Promises and challenges of generative artificial intelligence for human learning](https://mlsanity.com/paper/2408.12143/) (2024)
- [A Theory-driven and AI-enhanced Simulation Platform for Cultivating Nutrition Literacy](https://mlsanity.com/paper/2507.02138/) (2025)

## Abstract (arXiv)

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.