Exploring collaborative patterns in generative AI-supported collaborative learning: effects on knowledge construction and performance

Jiayao Zeng

doi:10.1007/s11423-026-10737-5

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Exploring collaborative patterns in generative AI-supported collaborative learning: effects on knowledge construction and performance

2026-09-29

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DOI：10.1007/s11423-026-10737-5

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Abstract

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The integration of generative AI (GAI) into collaborative learning has drawn increasing attention for its potential to reshape group dynamics and support collaborative knowledge construction (CKC). Yet, how CKC unfolds in GAI-supported contexts, particularly regarding interaction patterns, regulatory processes, and task outcomes, remains insufficiently understood. Grounded in group-regulated learning perspectives, this study examines how GAI -supported collaborative processes relate to students’ collaborative knowledge construction. The study analyzes detailed interaction data collected from three-member collaborative groups across two face-to-face tasks, yielding 24 group-task instances. Using a multi-method analytical framework that combines discourse analysis, hierarchical clustering, epistemic network analysis, process mining, and summative assessments, we identify four exploratory collaborative patterns: (1) the positive regulation-oriented pattern (Cluster 1), characterized by medium-level performance and more complex knowledge construction; (2) the negative regulation-oriented pattern (Cluster 4), also linked to medium-level performance and more complex CKC; (3) the socio-emotionally driven regulation pattern (Cluster 2), associated with low-level performance and simpler knowledge construction; and (4) the strategy-with-AI and monitoring-oriented regulation pattern (Cluster 3), linked to high performance and less complex CKC than Clusters 1 and 4, yet still showing more complexity than Cluster 2. Based on these findings, the study concludes with pedagogical implications, providing guidance for educators and instructional designers to optimize GAI-supported collaborative learning, fostering greater student engagement, and enhancing knowledge construction.

Keywords:

Computer-supported collaborative learning

Collaborative knowledge construction

Group regulated learning

Collaborative pattern

Multi-method analytical approach

Generative artificial intelligence