The impact of artificial intelligence on students' metacognition: a systematic review

Given the critical role of metacognition in academic achievement and the rapid integration of Artificial Intelligence (AI) in educational settings, understanding the interplay between the two has become essential. In recent years, a growing number of studies have examined their relationship, creating a pressing need for a systematic review to synthesize the accumulating evidence. This review adhered to the PRISMA guidelines, involving the search and screening of papers from three major databases (Web of Science, Scopus, EBSCOhost). A total of 22 empirical studies were included in the final synthesis. Data extraction and thematic analysis were employed to identify key impacts and underlying interaction patterns. This review distinguishes between Learning-Oriented AI (LAI) and General-Purpose AI (GAI) to organize the evidence. The findings suggest that LAI was more often associated with Guided Interaction, in which AI provides guiding feedback without directly supplying answers, whereas GAI was more commonly associated with Direct Feedback Interaction, in which AI provides answers or explicitly corrects errors. Across both categories, positive findings were reported for several metacognitive outcomes, including regulation, awareness, and shared metacognition. However, some GAI studies also reported that students relied excessively on AI, particularly when they accepted direct answers from AI without sufficient reflection. Overall, the findings highlight the importance of interaction design and teacher guidance in shaping how AI supports students' metacognitive development.