MODELS OF CONTEXTUALIZED PEDAGOGICAL ACTIVITIES IN MATHEMATICS MEDIATED BY AI MOBILE APPLICATIONS: A REVIEW FROM SITUATED COGNITION

Abstract

The substandard performance in mathematics observed in these contexts underscores the necessity for pedagogical approaches that integrate students' sociocultural contexts with the incorporation of novel technologies. This paper presents an analysis of the characteristics and models of contextualized educational activities, grounded in situated cognition and facilitated by educational mobile applications supported by artificial intelligence (AI), which are pertinent for eighth-grade students in public schools in the municipality of Ciénaga (Magdalena, Colombia). The review was conducted in a systematic manner and includes international, national, and regional precedents published between 2020 and 2025, focusing on research that intertwines situated learning, meaningful learning, and adaptive technology mediation. The findings indicate that educational applications such as GeoGebra Suite, Khan Academy, Photomath, and Socratic facilitate the development of didactic sequences that are centered on competencies (reasoning, problem solving, modeling, representation, and communication) and are aligned with the standards established by the Ministry of National Education and the Saber tests. Furthermore, successful initiatives that employ problem-based learning (PBL) and project-based learning (rPBL) have been identified. These initiatives, supported by artificial intelligence (AI), have been shown to enhance motivation and performance in mathematics. In summary, the integration of contextualized activities with mobile AI emerges as a promising approach to address the pedagogical gap in low-income communities. This approach is not only innovative but also has the potential to be replicated and aligned with educational quality policies, thereby contributing to the enhancement of educational outcomes in these communities.