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

TL;DR

This paper investigates how to design generative AI systems integrated with group awareness tools to enhance socially shared metacognition in collaborative tasks, emphasizing autonomous regulation and effective group learning.

Contribution

It introduces preliminary design principles for GenAI-augmented group awareness tools aimed at fostering autonomous social metacognition in collaborative environments.

Findings

Design principles for GenAI-augmented GATs to support SSM.

Potential to improve autonomous regulation in group work.

Risks of over-reliance on AI-generated instructions.

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.