The impact of metacognitive scaffolding on deep learning in a GenAI-supported learning environment

Abstract

Metacognitive scaffolding plays a pivotal role in fostering deep learning. However, the application of generative artificial intelligence (GenAI) in educational practice involves certain challenges, such as thought substitution, superficial cognitive processing, and impaired deep learning. Consequently, investigating the influence of metacognitive scaffolding on deep learning within the GenAI context was the central focus of this study. This study employed a quasi-experimental research design to investigate the impacts of metacognitive scaffolding on learners’ learning approaches, cognitive proficiency levels, and academic achievements in a GenAI learning environment, thereby elucidating the ability of metacognitive scaffolding to promote deep learning. The Mann‒Whitney U test was used to examine the disparities in learning methodologies and academic performance between the experimental and control groups. The SOLO cognitive level framework was used to code transcripts of the dialogs between learners and GenAI, and epistemic network analysis was employed to examine the distribution of cognitive levels between these two groups. These findings revealed that for the learners in the experimental group, the use of metacognitive scaffolding had a significantly positive deepening effect on their tendencies toward various learning approaches and on their cognitive hierarchy. Moreover, the experimental group exhibited better academic performance.

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