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.

翻译：社会共享元认知（SSM）指协作问题解决中对联合认知过程的集体监控与调节，是有效知识工作与学习的关键。基于生成式人工智能（GenAI）的系统为支持SSM提供了新机遇，但新兴证据表明，设计不当的系统可能促使团队过度依赖AI生成的显性指令，削弱其发展自主调节过程的能力。群体感知工具（GATs）通过成熟的设计原则应对这一挑战：使社会与认知感知信息可视化，突出成员差异以引发认知冲突，触发自主阐述与讨论，从而隐性引导自主SSM的涌现。本文通过初步文献检索，探索GenAI增强型GATs在协作工作与学习中支持自主SSM的设计，并提出初步设计原则以供讨论。