Building Regulation Capacity in Human-AI Collaborative Learning: A Human-Centred GenAI System

2026-04-11

AI Summary

This study addresses the unclear role of generative artificial intelligence (GenAI) in supporting co-regulated learning (CoRL) and socially shared regulated learning (SSRL) within small-group collaborative settings. It proposes a human-centered, teacher-in-the-loop GenAI collaborative learning system that integrates activity generation, process-oriented intra-group support agents, and an embedded learning analytics dashboard. By leveraging interaction trace tracking and real-time feedback mechanisms grounded in CoRL/SSRL theoretical frameworks, the research investigates human-AI coordination dynamics. The project aims to uncover how GenAI reconfigures collaborative regulation patterns, provide empirical evidence at the process level, enhance group regulatory capacity and collaborative performance across varying levels of AI involvement, and identify effective human-AI collaborative regulation paradigms.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 impact: it first identifies how GenAI reshapes regulation patterns and which patterns indicate more effective Human-AI collaboration, then builds an integrated GenAI system that targets these patterns, and finally evaluates whether the GenAI system improves regulation capacity and group performance across varying levels of GenAI involvement. Expected contributions include a teacher-in-the-loop system for Human-AI collaboration and process-level evidence on how GenAI reconfigures CoRL and SSRL in group work.

Problem

• collaborative learning
• generative AI
• co-regulation
• socially shared regulation
• regulation capacity

Innovation

• Generative AI
• Collaborative Learning
• Socially Shared Regulation
• Learning Analytics Dashboard
• Human-AI Collaboration