Peranan Metakognisi dalam Penggunaan Artificial Intelligence Pada Pembelajaran Matematika - Telaumbanua, Yakin Niat (Papanda, 2026)
The development of Artificial Intelligence (AI) has introduced both new opportunities and challenges in mathematics learning. Various AI-based systems, such as intelligent tutoring systems, generative chatbots, and problem-solving assistants, are capable of providing adaptive and instant cognitive support. However, the use of AI without reflective capabilities may lead to students’ dependency and superficial learning. This study aims to analyze the role of metacognition in the use of AI in mathematics learning and to formulate a conceptual model explaining the relationship between metacognitive processes and the quality of students’ interaction with AI. This study employed a qualitative approach using a systematic literature review design consisting of five main stages: formulation of research focus and questions, literature search strategy, inclusion and exclusion criteria, article selection process, and data analysis and synthesis. The articles analyzed included 32 Scopus-indexed journal articles published between 2019 and 2024, complemented by eight classical and contemporary metacognition theory books relevant to the research focus. Data analysis was conducted through thematic analysis to identify the roles of metacognitive planning, monitoring, evaluation, and control in students’ interactions with AI. The findings indicate that metacognition functions as a primary mediator of the effectiveness of AI use in mathematics learning. Students with high metacognitive ability tend to use AI strategically, critically, and reflectively, whereas low metacognitive ability is associated with uncritical acceptance of AI outputs and a higher risk of overreliance. This study proposes a conceptual model positioning AI as an object of metacognitive regulation, in which monitoring and control processes at the meta-level mediate the quality of mathematical problem solving. These findings contribute theoretically to the advancement of metacognition research and provide practical implications for designing AI-supported mathematics learning that promotes self-regulation and reflective thinking.