Improving Young Learners with Copilot: The Influence of Large Language Models (LLMs) on Cognitive Load and Self-Efficacy in K-12 Programming Education

The integration of Large Language Models (LLMs) such as Microsoft Copilot in K-12 programming education has demonstrated the potential to alleviate cognitive load and enhance self-efficacy among young learners. This study examined the impact of Copilot-assisted instruction on cognitive load and self-efficacy in primary school students engaged in programming education. Guided by Cognitive Load Theory and self-efficacy principles, the study employed a quasi-experimental design involving primary school students in Macao. Participants completed pre- and post-tests measuring cognitive load and self-efficacy using the Chinese version of the Cognitive Load Scale (CCLS) and the General Self-Efficacy Scale (GSES). The results indicated significantly reduced students' cognitive load across the mental load and mental effort dimensions. Concurrently, self-efficacy scores exhibited a statistically significant increase. Correlation analysis revealed a strong negative relationship between cognitive load and self-efficacy, suggesting that students' confidence in programming tasks improved as cognitive load decreased. These findings highlighted the potential of AI-driven educational tools in optimizing learning environments, reducing cognitive demands, and fostering positive academic self-perception in early programming education.