Enhancing Students’ Metacognition With Innovative IA-Based Metacognitive Reflective Learning Tool

Abstract

Intelligence augmentation can offer personalized learning resources and pathways tailored to each student’s unique characteristics and needs. Among these advancements, the large language model (LLM) agent has ushered in a new revolution in education. In this study, we constructed a metacognitive reflective learning scaffold (MRLS) grounded in metacognitive theory and reflective learning principles to provide conceptual support for students during their reflective practices. In addition, we developed a metacognitive reflective learning agent (MRLA) on the Coze platform designed to deliver personalized guidance and assistance throughout the reflective learning process. We conducted a 16-week $2 \times 2$ quasi-experiment study at Z University in China, where participants were randomly assigned to four groups. Throughout the research process, we collected dialogue data from students using the Coze platform, as well as reflection reports submitted via the XueXiTong platform for quantitative analysis. Empirical results demonstrated that both the MRLS and MRLA significantly enhanced students’ metacognition, indicated that the MRLS offers precise guidance for students’ reflective learning processes, enabling them to better comprehend and articulate their reflections. The MRLA equips students with more convenient, efficient, and intelligent resources, significantly augmenting the provision of metacognitive training support that would otherwise be provided by teachers. This study emphasizes the validity and necessity of MRLS and MRLA for the cultivation of students’ metacognitive ability and provides insights for the future application of LLM agent and learning scaffolds for optimizing students’ learning process.