Student engagement in collaborative learning with AI agents in an LLM-empowered learning environment: A cluster analysis (2503.01694v1)

Abstract: Integrating LLM models into educational practice fosters personalized learning by accommodating the diverse behavioral patterns of different learner types. This study aims to explore these learner types within a novel interactive setting, providing a detailed analysis of their distinctive characteristics and interaction dynamics. The research involved 110 students from a university in China, who engaged with multiple LLM agents in an LLM-empowered learning environment, completing coursework across six modules. Data on the students' non-cognitive traits, course engagement, and AI interaction patterns were collected and analyzed. Using hierarchical cluster analysis, the students were classified into three distinct groups: active questioners, responsive navigators, and silent listeners. Epistemic network analysis was then applied to further delineate the interaction profiles and cognitive engagement of different types of learners. The findings underscore how different learner types engage with human-AI interactive learning and offer practical implications for the design of adaptive educational systems.