The Impact of Artificial Intelligence-based Human-Machine Collaborative Learning on Self-regulated Learning: A Meta-analysis Based on 27 Experimental and Quasi-experimental Studies

The human-machine collaborative learning model exemplifies the deep integration of artificial intelligence (AI) and education in the intelligent era, signaling a trend for the future of teaching and learning. However, the impact of AI-based human-machine collaborative learning models on metacognition within the educational field remains unclear. This study conducted a meta-analysis of 27 experimental and quasi-experimental studies sourced from the Web of Science Core Collection. Utilizing CMA3.0 software, the analysis systematically examined practical outcomes across various dimensions, including instructional content, learner grade levels, and the duration of technology applications. The results indicate that AI-based human-machine collaborative learning models significantly enhance learners’ self-regulated learning (SRL) by improving learning outcomes and fostering task strategy deployment, metacognitive monitoring, and help-seeking abilities. Furthermore, this paper offers strategic recommendations for optimizing the role of AI in promoting SRL, aiming to provide insights for the deeper integration of AI into education and the advancement of high-quality educational development.