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<p>In collaborative learning, regulation is crucial for meaningful and effective collective learning. As generative artificial intelligence (AI) becomes readily available to learners as an external, on-demand information resource, regulatory behavior dynamics may shift from information seeking toward evaluating, selecting, integrating, and coordinating externally generated content within group discourse. This study examined the learners’ behavioral dynamics in their cognitive, task, and emotional regulation and investigated the interplay of individual performance and behavior transition patterns against the backdrop of group achievement in an AI-supported collaborative learning environment. A total of 126 undergraduate and graduate students participated in a face-to-face learning task, with access to online AI tools at the learners’ discretion. Audio recordings were transcribed into 16,816 semantic units for content analysis. Using <i>k</i>-means clustering, the research identified four distinct learner types, namely evaluative, curious, expressive, and passive. Lag sequential analysis (LSA) results showed divergent behavioral patterns emerged between high- and low-performance groups and individuals. Group performance level did not warrant similar individual performance levels. The study highlights the complex interplay between individual and group behaviors and their impact on performance, in particular the behaviors of high-performance individuals in low-performing groups and vice versa. Notably, these patterns underscore how group outcomes can hinge on how contributions are regulated and coordinated, rather than on information availability alone. Collectively, the findings suggest that, in AI-supported collaboration, performance is associated with the learner’s capacity to critically appraise and purposely appropriate information among the group members and to align its use with task goals and social emotional coordination. This research offers insights for the facilitators on grouping strategies, differentiated scaffold design for learner types, and intervention considerations for low-performance groups and individuals. Limitations and future research directions are also discussed.</p>

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Mapping behavioral dynamics in AI-supported CSCL: Analyzing cognitive, task, and emotional regulation patterns and their impact on performance

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Shurui Gao,
  • Weijia Chen,
  • Fengwu Han,
  • Fati Wu

摘要

In collaborative learning, regulation is crucial for meaningful and effective collective learning. As generative artificial intelligence (AI) becomes readily available to learners as an external, on-demand information resource, regulatory behavior dynamics may shift from information seeking toward evaluating, selecting, integrating, and coordinating externally generated content within group discourse. This study examined the learners’ behavioral dynamics in their cognitive, task, and emotional regulation and investigated the interplay of individual performance and behavior transition patterns against the backdrop of group achievement in an AI-supported collaborative learning environment. A total of 126 undergraduate and graduate students participated in a face-to-face learning task, with access to online AI tools at the learners’ discretion. Audio recordings were transcribed into 16,816 semantic units for content analysis. Using k-means clustering, the research identified four distinct learner types, namely evaluative, curious, expressive, and passive. Lag sequential analysis (LSA) results showed divergent behavioral patterns emerged between high- and low-performance groups and individuals. Group performance level did not warrant similar individual performance levels. The study highlights the complex interplay between individual and group behaviors and their impact on performance, in particular the behaviors of high-performance individuals in low-performing groups and vice versa. Notably, these patterns underscore how group outcomes can hinge on how contributions are regulated and coordinated, rather than on information availability alone. Collectively, the findings suggest that, in AI-supported collaboration, performance is associated with the learner’s capacity to critically appraise and purposely appropriate information among the group members and to align its use with task goals and social emotional coordination. This research offers insights for the facilitators on grouping strategies, differentiated scaffold design for learner types, and intervention considerations for low-performance groups and individuals. Limitations and future research directions are also discussed.