INCLUSÃO E DESIGUALDADES NO USO DE ALGORITMOS EDUCACIONAIS: FUNDAMENTOS DA JUSTIÇA ALGORÍTMICA E IMPLICAÇÕES PARA A PRÁTICA PEDAGÓGICA

This article discusses how educational algorithms, increasingly embedded in digital learning, assessment, and school management platforms, may generate unequal effects on students when they rely on historical data shaped by structural social asymmetries. Drawing on the foundations of algorithmic justice, the paper examines risks related to bias, opacity, and the normalization of automated classifications, arguing that promises of personalization can constrain learning trajectories and intensify exclusion. From the perspective of pedagogical practice, it contends that responsible use of these technologies requires transparency, auditability, and teacher mediation, preventing automated decisions from replacing professional pedagogical judgment. The discussion also highlights the importance of institutional policies for data governance, continuing teacher education, and clear criteria for adopting AI-based educational systems, with an emphasis on equity and the protection of rights. The article concludes that inclusion in algorithm-mediated contexts depends on a critical and contextualized approach that turns technology into an object of pedagogical reflection and social oversight, reducing the reproduction of inequalities and strengthening fairer educational practices.