Exploring the Design of GenAI-Based Systems to Support Socially Shared Metacognition

2026-03-09

AI Summary

This study addresses the risk that generative artificial intelligence (GenAI), when poorly designed, may foster user overreliance and undermine groups’ self-regulatory capacity in collaborative settings involving socially shared metacognition. To mitigate this, the authors propose an initial set of design principles integrating Group Awareness Tools (GATs) with GenAI, aiming to subtly scaffold the development of socially shared metacognition without excessive AI intervention. Key mechanisms include visualizing cognitive discrepancies and stimulating productive cognitive conflict, thereby promoting group autonomy through implicit guidance. Grounded in a comprehensive literature review, the proposed framework articulates strategic approaches to position GenAI as an enabler rather than a director of collaborative processes, offering a theoretical foundation for future empirical investigations and system development.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Socially shared metacognition (SSM) refers to the collective monitoring and regulation of joint cognitive processes in collaborative problem-solving, and is essential for effective knowledge work and learning. Generative AI (GenAI)-based systems offer new opportunities to support SSM, but emerging evidence suggests that poorly designed systems can encourage over-reliance on AI-generated explicit instruction and erode groups' capacity to develop autonomous regulatory processes. Group awareness tools (GATs) address this challenge through established design principles that make social and cognitive awareness information visible, highlight differences between group members to create cognitive conflict, and trigger autonomous elaboration and discussion, thereby implicitly guiding autonomous SSM emergence. This paper explores the design of GenAI-augmented GATs to support autonomous SSM in collaborative work and learning through an initial literature search, presenting preliminary design principles for discussion.

Problem

• socially shared metacognition
• generative AI
• group awareness tools
• autonomous regulation
• collaborative learning

Innovation

• Generative AI
• Socially Shared Metacognition
• Group Awareness Tools
• Autonomous Regulation
• Collaborative Learning