DOI: 10.1111/bjet.70096 ISSN: 0007-1013

Generative AI as a team moderator in CSCL : Facilitating students' multidimensional engagement through adaptive scaffolding for socially shared regu

Dengkang Chen, Yi Zhang, Heng Luo, Fucheng Guo, Yijie Lu, Xiaoge Guo

Abstract

Socially shared regulation of learning (SSRL) is essential for successful collaboration, yet effectively scaffolding it remains challenging because regulatory needs are dynamic and context‐dependent. While generative AI (GenAI) offers potential for adaptive support, current applications in collaborative learning largely position it as a content‐oriented tutor for knowledge explanation or idea generation, leaving its role as a process‐oriented team moderator for scaffolding SSRL processes underexplored. To address this gap, we conducted a 16‐week quasi‐experiment with 64 undergraduates in China, comparing adaptive SSRL scaffolding delivered by a GenAI agent in the adaptive‐SSRL condition with static scaffolding via structured digital worksheets in the static‐SSRL condition. Survey data and group discussion transcripts were analysed using statistical methods and epistemic network analysis. Results indicated several differences in students' multidimensional engagement between the two conditions: (1) Behaviourally, the adaptive‐SSRL group showed significantly higher participation equality, although participation intensity did not differ significantly between groups. (2) Cognitively, the adaptive‐SSRL group demonstrated deeper cognitive engagement, reflected in more advanced cognitive presence phases and more integrative, solution‐oriented discourse patterns. (3) Emotionally, the adaptive‐SSRL group reported significantly higher overall emotional engagement, particularly feeling more relaxed during collaboration and more connected with peers. These findings extend human–AI collaboration research by showing that GenAI can function as an SSRL‐informed team moderator rather than a content‐oriented tutor. They also offer design implications for GenAI‐enabled adaptive SSRL scaffolding and human–AI role division in CSCL.

Practitioner notes

What is already known about this topic

Socially shared regulation of learning (SSRL) is critical for successful collaboration and is closely associated with the quality of student engagement.

While static scaffolds (eg, worksheets) provide a basic regulatory structure, they fail to adapt to the dynamic and emergent needs of collaborating teams.

Generative AI (GenAI) is increasingly used for personalised content support, yet its potential as a process‐oriented team moderator remains underexplored.

What this paper adds

This study introduced SSRLAgent, a GenAI agent grounded in SSRL theory and established principles of collective metacognitive support, to provide adaptive and interactive regulatory scaffolding.

In a 16‐week quasi‐experiment, we compared GenAI‐enabled adaptive SSRL scaffolding (SSRLAgent) with static scaffolding (structured digital worksheets) in CSCL.

Adaptive‐SSRL scaffolding was associated with higher‐quality engagement: greater participation equality, deeper cognitive engagement and higher overall emotional engagement.

Implications for practice and/or policy

The study underscores the necessity of shifting SSRL scaffolding from static, ‘one‐size‐fits‐all’ support to adaptive GenAI interventions to improve collaborative engagement.

The findings provide design guidance for developing SSRL agents that are embedded in the shared interaction space and responsive to groups' moment‐to‐moment regulatory needs.

The study suggests a productive human–AI division of labour: GenAI agents can support process coordination and regulatory prompting, while teachers and peers remain essential for relationally complex forms of support that collaborative learning requires.

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