Modeling Analysis of Student-GenAI Interaction Behavior in GenAI-Supported Metacognitive Regulation Learning

Abstract

Generative artificial intelligence (GenAI) has demonstrated significant effectiveness in supporting students’ metacognitive regulation. However, current research has yet to examine the patterns of interaction behaviors between students and GenAI in learning contexts. This study developed a GenAI-supported metacognitive regulation learning system and implemented it in teaching practice. Through cluster analysis to model the interactions between students and GenAI across multiple learning tasks, this study identified four distinct interaction patterns, namely, active, balanced, disengaged, and detached patterns. Students exhibiting the active pattern and the balanced pattern demonstrated higher self-efficacy and better academic achievement, as well as richer and more complete behavioral sequences of metacognitive regulation, compared to those with the disengaged pattern and the detached pattern. These findings provide valuable insights for the design and implementation of the GenAI-supported learning environment.

Keywords

generative artificial intelligence (GenAI) / metacognitive regulation / student-GenAI interaction / behavior analysis

RIGHTS & PERMISSIONS

Higher Education Press