Resumo

CONTRERAS CONTRERAS, Fortunato  e  OLAYA GUERRERO, Julio Cesar. Educational filters based on artificial intelligence: transformations in teaching and teacher-student interaction. Rev. Simón Rodríguez [online]. 2025, vol.5, n.10, pp.225-241.  Epub 30-Out-2025. ISSN 3006-1385.  https://doi.org/10.62319/simonrodriguez.v.5i10.58.

AI-based educational filters mediate student access, sequencing, assessment, and support using learning data. This study aims to analyze the evolution, effectiveness, and pedagogical transformations of AI-based educational filters and their impact on teaching and teacher-student interaction. The approach is qualitative, employing a narrative-analytical review of empirical evidence, randomized controlled trials (RCTs), systematic reviews, and institutional frameworks. The results indicate significant improvements in learning and student engagement with time efficiency. The RCT in higher education shows higher medians with AI tutors (4.5 vs. 3.5), greater engagement (4.1 vs. 3.6), and shorter time (49 vs. 60 min). Intelligent tutoring systems in K-12 demonstrate positive effects on self-regulation. Multimodal pipelines report high specificity (99.06%) with variable sensitivity. The study concludes that educational filters constitute transformative infrastructure that personalizes and optimizes learning when they preserve human interaction and are accompanied by robust governance frameworks.

Palavras-chave : Higher education; Educational filters; Artificial intelligence; Intelligent tutoring systems.

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