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<p>The Brazilian Textbook Program (<i>Programa Nacional do Livro e do Material Didático</i>—PNLD) contributes to providing equitable access to quality educational resources in public schools. However, various factors such as socioeconomic conditions, environmental challenges, and health crises can disrupt the evaluation, distribution, and use of educational resources, undermining effectiveness. This article explores the potential of augmented intelligence—the integration of human and Artificial Intelligence (AI)—as a solution to enhance resilience in the PNLD’s evaluation process. The study presents an AI-based system designed to streamline the anonymization of digital textbooks, a critical step in ensuring unbiased evaluation. By reducing the time and resources required, this AI-driven approach promises to improve the textbook selection process’s efficiency, impartiality, and equity. Furthermore, we discuss the scalability of this solution and its potential for broader application in educational resource management across various contexts.</p>

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Expanding the resilience of the Brazilian education system by supporting the evaluation of digital textbooks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月13日

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  • Ranilson Paiva,
  • Janaína Xisto,
  • Álvaro Sobrinho,
  • Alan Silva,
  • Felipe Sarmento,
  • Filipe Recch,
  • Sidarta Tenório,
  • Andressa Carvalho,
  • Ig Bittencourt,
  • Seiji Isotani

摘要

The Brazilian Textbook Program (Programa Nacional do Livro e do Material Didático—PNLD) contributes to providing equitable access to quality educational resources in public schools. However, various factors such as socioeconomic conditions, environmental challenges, and health crises can disrupt the evaluation, distribution, and use of educational resources, undermining effectiveness. This article explores the potential of augmented intelligence—the integration of human and Artificial Intelligence (AI)—as a solution to enhance resilience in the PNLD’s evaluation process. The study presents an AI-based system designed to streamline the anonymization of digital textbooks, a critical step in ensuring unbiased evaluation. By reducing the time and resources required, this AI-driven approach promises to improve the textbook selection process’s efficiency, impartiality, and equity. Furthermore, we discuss the scalability of this solution and its potential for broader application in educational resource management across various contexts.