How GenAI Mentor Configurations Shape Early Collaborative Dynamics: A Classroom Comparison of Individual and Shared Agents

2026-03-12

AI Summary

This study investigates how the configuration of generative artificial intelligence (GenAI) in collaborative learning environments shapes students’ co-regulation processes. Comparing two structural designs—shared versus individual AI tutors—the research employs multilevel discourse coding, lag sequential analysis (LSA), and ordinal network analysis (ONA) to examine interaction patterns, regulatory behaviors, and teacher facilitation among middle school groups engaged in creative problem solving. For the first time, AI configuration is treated as a structural variable, revealing its role in reconfiguring classroom collaboration ecologies: shared AI fosters group-level reasoning coherence and alignment of regulatory states, whereas individual AI, while enhancing exploration and evaluation, leads to fragmented interactions that necessitate greater teacher intervention.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Natural Language Processing: Generation
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Generative artificial intelligence (GenAI) is increasingly embedded in computer-supported collaborative learning (CSCL), yet little empirical research has unpacked how different configurations of AI participation reshape collaborative processes. This study investigates how GenAI configuration shapes collaborative regulation in authentic classroom settings. Two eighth-grade classes engaged in small-group creative problem-solving under two conditions: a shared-AI configuration, in which each group interacted with a single AI mentor, and an individual-AI configuration, in which each student accessed a personal AI instance. Using multi-layer discourse coding combined with lag sequential analysis (LSA) and ordered network analysis (ONA), we examined interaction distribution, AI-student coupling, shared regulation processes, and teacher orchestration. Results reveal distinct regulatory dynamics across configurations. Shared AI access promoted convergence-oriented collaboration, with stronger alignment of shared regulatory states and more coordinated group-level reasoning. In contrast, individual AI access distributed support across learners, producing more exploratory and evaluative cycles but also more fragmented interaction patterns, accompanied by increased teacher intervention to manage divergence. These findings suggest that AI configuration functions as a structural design variable that reorganizes the regulatory ecology of classroom collaboration.

Problem

• GenAI configuration
• collaborative regulation
• computer-supported collaborative learning
• classroom collaboration
• AI mentor

Innovation

• GenAI configuration
• collaborative regulation
• shared AI mentor
• individual AI mentor
• classroom collaboration