Building Regulation Capacity in Human-AI Collaborative Learning: A Human-Centred GenAI System
Yujing Zhang, Jionghao Lin

TL;DR
This doctoral project develops a GenAI-supported collaborative learning system to enhance group regulation processes like goal setting and monitoring, aiming to improve human-AI teamwork.
Contribution
It introduces an integrated system with activity generation, support agents, and analytics to strengthen socially distributed regulation in group learning with GenAI.
Findings
Identifies how GenAI reshapes regulation patterns in group work
Builds a system targeting effective regulation patterns
Evaluates the system's impact on regulation capacity and performance
Abstract
Collaborative learning works when groups regulate together by setting shared goals, coordinating participation, monitoring progress, and responding to breakdowns through co-regulation (CoRL) and socially shared regulation (SSRL). As generative AI (GenAI) enters group work, however, it remains unclear whether and how it supports these socially distributed regulation processes. This doctoral project proposes a GenAI-supported collaborative learning system grounded in CoRL and SSRL to strengthen groups' socially distributed regulation capacity. The system links three components: (1) group activity generation; (2) an in-group support agent that provides process-focused prompts without giving solutions; and (3) an embedded learning analytics dashboard that turns interaction traces into timely summaries for monitoring and decision making. The project progresses from mechanism to design to…
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