Mapping the Interplay Between Perceived Personal Skills, Self-Regulated Learning, and Metacognitive Engagement with Generative AI
TL;DR
The findings indicate that perceived personal skills and perceived usefulness are the strongest predictors of self-reported metacognitive use of GenAI, and learning motivation emerged as a negative predictor, suggesting that motivation does not necessarily translate into effective engagement with GenAI-based learning.
Abstract
The growing presence of generative artificial intelligence (GenAI) in learning environments highlights a need to understand self-regulated learning (SRL) as these technologies enable new pathways for independent learning. This study examined the cognitive and personal factors associated with the self-reported metacognitive use of GenAI within the SRL framework, focusing on perceived self-regulation skills, perceived usefulness, and learning motivation. An online survey was conducted among 1265 participants recruited through snowball sampling. The findings indicate that perceived personal skills and perceived usefulness are the strongest predictors of self-reported metacognitive use of GenAI. In contrast, learning motivation emerged as a negative predictor, suggesting that motivation does not necessarily translate into effective engagement with GenAI-based learning. Additionally, perceived usefulness partially mediated the relationship between motivation and metacognitive use. The findings extend SRL theory to GenAI-driven learning environments and highlight the importance of fostering learners’ self-regulation skills and awareness of the value of GenAI for effective adoption.