ACTIVE METHODOLOGIES MEDIATED BY ARTIFICIAL INTELLIGENCE IN MATHEMATICS EDUCATION: ANALYSIS OF PEDAGOGICAL PRACTICES GUIDED BY PROBLEM SOLVING

Abstract

The incorporation of active methodologies mediated by Artificial Intelligence (AI) in Mathematics teaching in Basic Education has emerged as a strategic response to the historical limitations of traditional approaches centered on content transmission and the mechanical reproduction of procedures. This study aims to critically analyze pedagogical practices guided by problem solving when articulated with the use of AI tools, investigating the extent to which such integrations foster meaningful learning and the development of complex cognitive skills. It is assumed that problem solving, as a structuring axis of contemporary Mathematics Education, finds in AI mediation expanded possibilities for personalization, immediate feedback, and scenario simulation, provided that these are grounded in consistent theoretical foundations and guided by pedagogical intentionality. Methodologically, this is a qualitative study of an analytical-interpretative nature, based on a systematic review of recent literature and normative documents, such as the National Common Curricular Base (BNCC). The results indicate that AI can enhance active methodologies by promoting dynamic interaction between student, problem, and knowledge, expanding opportunities for investigation, argumentation, and decision-making. However, risks associated with the uncritical use of these technologies are also identified, such as the automation of reasoning, superficial learning, and cognitive dependency. The analysis demonstrates that the effectiveness of these practices fundamentally depends on teacher mediation, clarity of learning objectives, and alignment with the principles of meaningful learning. It is concluded that the integration of active methodologies and AI in Mathematics teaching should not be understood as a replacement of pedagogical practices, but as a qualitative reconfiguration of the educational process, requiring specific teacher training, critical reflection, and ethical commitment. Thus, the use of AI, when aligned with problem solving and curricular guidelines, can contribute to the development of more autonomous, critical students capable of mobilizing mathematical knowledge in diverse contexts, provided that its implementation is carefully planned and pedagogically grounded.