IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v12y2025i1d10.1057_s41599-025-05489-1.html
   My bibliography  Save this article

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

Author

Listed:
  • Ranilson Paiva

    (Federal University of Alagoas
    Núcleo de Excelência em Tecnologias Sociais)

  • Janaína Xisto

    (Federal University of Alagoas
    Núcleo de Excelência em Tecnologias Sociais)

  • Álvaro Sobrinho

    (Núcleo de Excelência em Tecnologias Sociais
    Universidade Federal do Agreste de Pernambuco)

  • Alan Silva

    (Núcleo de Excelência em Tecnologias Sociais
    University of Oxford)

  • Felipe Sarmento

    (Federal University of Alagoas
    Núcleo de Excelência em Tecnologias Sociais)

  • Filipe Recch

    (University of Oxford
    University of Pittsburgh)

  • Sidarta Tenório

    (Federal University of Alagoas
    Núcleo de Excelência em Tecnologias Sociais)

  • Andressa Carvalho

    (Núcleo de Excelência em Tecnologias Sociais)

  • Ig Bittencourt

    (Federal University of Alagoas
    Núcleo de Excelência em Tecnologias Sociais)

  • Seiji Isotani

    (Núcleo de Excelência em Tecnologias Sociais
    Universidade de São Paulo)

Abstract

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.

Suggested Citation

  • Ranilson Paiva & Janaína Xisto & Álvaro Sobrinho & Alan Silva & Felipe Sarmento & Filipe Recch & Sidarta Tenório & Andressa Carvalho & Ig Bittencourt & Seiji Isotani, 2025. "Expanding the resilience of the Brazilian education system by supporting the evaluation of digital textbooks," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-9, December.
  • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05489-1
    DOI: 10.1057/s41599-025-05489-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-025-05489-1
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-025-05489-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K, 2019. "Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda," International Journal of Information Management, Elsevier, vol. 48(C), pages 63-71.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    3. Thamsanqa Keith Miya & Irene Govender, 2022. "UX/UI design of online learning platforms and their impact on learning: A review," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 11(10), pages 316-327, December.
    4. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Majd Oteibi & Adam Tamimi & Kaneez Abbas & Gabriel Tamimi & Danesh Khazaei & Hadi Khazaei, 2024. "Advancing Digital Health using AI and Machine Learning Solutions for Early Ultrasonic Detection of Breast Disorders in Women," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(11), pages 518-527, November.
    2. Riccardo Zanardelli, 2025. "The human-machine paradox: how collaboration creates or destroys value, and why augmentation is key to resolving it," Papers 2509.14057, arXiv.org, revised Nov 2025.
    3. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    5. Freddy Gabbay & Rotem Lev Aharoni & Ori Schweitzer, 2022. "Deep Neural Network Memory Performance and Throughput Modeling and Simulation Framework," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    6. Ting Wang & Boyang Zang & Chui Kong & Yigang Li & Xiaomin Yang & Yi Yu, 2025. "Intelligent and precise auxiliary diagnosis of breast tumors using deep learning and radiomics," PLOS ONE, Public Library of Science, vol. 20(6), pages 1-11, June.
    7. Sonika Darshan, 2024. "Data Mining for Disease Diagnosis: A Review of Machine Learning Approaches in Healthcare," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 716-726.
    8. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    9. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    10. Shang Li & Fei Yu & Shankou Zhang & Huige Yin & Hairong Lin, 2025. "Optimization of Direct Convolution Algorithms on ARM Processors for Deep Learning Inference," Mathematics, MDPI, vol. 13(5), pages 1-19, February.
    11. Dario Sipari & Betsy D. M. Chaparro-Rico & Daniele Cafolla, 2022. "SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis," IJERPH, MDPI, vol. 19(16), pages 1-27, August.
    12. Darko B. Vuković & Senanu Dekpo-Adza & Stefana Matović, 2025. "AI integration in financial services: a systematic review of trends and regulatory challenges," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-29, December.
    13. Walter Leal Filho & João Henrique Paulino Pires Eustachio & Andreea Corina Nita (Danila) & Maria Alzira Pimenta Dinis & Amanda Lange Salvia & Debby R. E. Cotton & Kamila Frizzo & Laís Viera Trevisan &, 2024. "Using data science for sustainable development in higher education," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(1), pages 15-28, February.
    14. Mariam Bilal, 2023. "Analysis of High-Dimensional and Complex Data, such as Genomic Data, Neuroimaging Data, and Text Data, Using Machine Learning and Dimension Reduction Techniques in Pakistan," Journal of Statistics and Actuarial Research, IPRJB, vol. 7(1).
    15. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.
    16. Oded Rotem & Tamar Schwartz & Ron Maor & Yishay Tauber & Maya Tsarfati Shapiro & Marcos Meseguer & Daniella Gilboa & Daniel S. Seidman & Assaf Zaritsky, 2024. "Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    17. Taneja, Anu & Arora, Anuja, 2019. "Modeling user preferences using neural networks and tensor factorization model," International Journal of Information Management, Elsevier, vol. 45(C), pages 132-148.
    18. Hanning Ying & Xiaoqing Liu & Min Zhang & Yiyue Ren & Shihui Zhen & Xiaojie Wang & Bo Liu & Peng Hu & Lian Duan & Mingzhi Cai & Ming Jiang & Xiangdong Cheng & Xiangyang Gong & Haitao Jiang & Jianshuai, 2024. "A multicenter clinical AI system study for detection and diagnosis of focal liver lesions," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    19. Nao Aisu & Masahiro Miyake & Kohei Takeshita & Masato Akiyama & Ryo Kawasaki & Kenji Kashiwagi & Taiji Sakamoto & Tetsuro Oshika & Akitaka Tsujikawa, 2022. "Regulatory-approved deep learning/machine learning-based medical devices in Japan as of 2020: A systematic review," PLOS Digital Health, Public Library of Science, vol. 1(1), pages 1-12, January.
    20. Cristian Simionescu & Adrian Iftene, 2022. "Deep Learning Research Directions in Medical Imaging," Mathematics, MDPI, vol. 10(23), pages 1-25, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05489-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/palcomms/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.