Designing Robot Interface States to Facilitate Human Metacognition

Meiying Li, Yate Ge, Weiwei Guo

Abstract: Metacognition has been recognized as a crucial factor in enhancing human learning and problem-solving capabilities. While robots can serve as assistive mediators to improve human metacognition in specific tasks, existing research lacks a comprehensive framework for implementing metacognitive theories in human-robot interaction design. In addressing that, we propose a two-level state-based framework that integrates metacognition regulation cycle with specific intervention strategies. Through analyzing metacognitive theories, we present a hierarchical structure where Level 1 states represent the metacognition cycle and Level 2 states provide strategic interventions. Our work bridges the gap between metacognitive theory and human-robot interaction design, contributing a flexible approach for developing metacognition-enhanced robotic interfaces.