ARTIFICIAL INTELLIGENCE AND EDUCATION: BETWEEN TECHNOLOGICAL INNOVATION AND ETHICAL CHALLENGES

Authors

  • Alessandra Barboza Barros Almeida Universidade Christian Business School
  • Davi Borges Duailibe Costa UNDB
  • Dayse Marinho Martins UFMA
  • Raíla Socorro de Oliveira UNIJUI
  • Edila Rose Barata de Lima UFPA
  • Fernanda Resende Gonçalves Faculdade Iguaçu
  • Cláudia Pelicao Camargo Bahia Universidade Luterana do Brasil
  • Analieze Aparecida Leopoldino UFSC
  • Ricardo Normando Ferreira de Paula UECE
  • Fernanda Nascimento Almeida IFBA
  • Luciane Batista Teixeira UFPI
  • Idelvan Nascimento da Silva UEMA
  • Leandro Alfredo Dos Santos Silva UFCG
  • Thiago Benitez de Melo UNIOESTE

DOI:

https://doi.org/10.66104/qkxnh791

Keywords:

Inteligência artificial; inovação pedagógica; ética digital; educação personalizada; tecnologias educacionais.

Abstract

The application of Artificial Intelligence (AI) in education redefines the contemporary teaching paradigm, expanding possibilities for personalized learning at scale. This study, designed as a theoretical essay based on an integrative literature review, discusses the potentialities, risks, and ethical dilemmas of integrating AI within the school context. The central objective is to investigate responsible implementation strategies using the Decision Engineering framework to couple AI algorithms with pedagogical flows. The methodology followed the six-stage protocol for integrative reviews, with critical evaluation via the JBI instrument. The analysis demonstrates that AI should not be seen merely as an isolated tool but as a value-creation platform that requires robust governance and ethics (privacy-by-design). It is concluded that the competitive advantage of institutions lies in the ability to mitigate algorithmic hallucinations and biases through educational MLOps practices, promoting personalized education that respects the individuals' autonomy and diversity.

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References

AMERSHI, S. et al. Software engineering for machine learning: a case study. ICSE SEIP, p. 291-300, 2019. DOI: https://doi.org/10.1109/ICSE-SEIP.2019.00042

ARMBRUST, M. et al. A view of cloud computing. Communications of the ACM, v. 53, n. 4, p. 50-58, 2010. DOI: https://doi.org/10.1145/1721654.1721672

ARMBRUST, M. et al. Delta Lake: high-performance ACID table storage over cloud object stores. VLDB, v. 13, n. 12, p. 3411-3424, 2020. DOI: https://doi.org/10.14778/3415478.3415560

ARMBRUST, M. et al. Lakehouse: a new generation of open platforms that unify data warehousing and advanced analytics. CIDR, 2021.

BARDIN, L. Análise de Conteúdo. São Paulo: Edições 70, 2016.

BRYNJOLFSSON, E.; MCAFEE, A. Machine, Platform, Crowd. New York: W. W. Norton, 2017.

CAGAN, M. INSPIRED: how to create tech products customers love. 2. ed. New Jersey: Wiley, 2018.

DAMA International. DAMA-DMBOK2: data management body of knowledge. 2. ed. 2017.

DAVENPORT, T. H.; HARRIS, J. G. Competing on Analytics: the new science of winning. Boston: Harvard Business Review Press, 2007.

DEHGHANI, Z. Data Mesh: delivering data-driven value at scale. Sebastopol: O’Reilly, 2022.

FRANÇA, J. L. Manual para normalização de publicações técnico-científicas. 8. ed. Belo Horizonte: Ed. UFMG, 2008.

GANDOMI, A.; HAIDER, M. Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, v. 35, n. 2, p. 137-144, 2015. DOI: https://doi.org/10.1016/j.ijinfomgt.2014.10.007

KREUZBERGER, R.; KÜHL, D.; POLZE, J. MLOps: a survey of techniques for operationalizing machine learning. ACM Computing Surveys, 2023.

LEWIS, P. et al. Retrieval-augmented generation for knowledge-intensive NLP. NeurIPS, 2020.

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY (NIST). AI Risk Management Framework 1.0. Gaithersburg: NIST, 2023.

PROVOST, F.; FAWCETT, T. Data Science for Business. Sebastopol: O’Reilly, 2013.

SCULLEY, D. et al. Hidden technical debt in machine learning systems. NIPS, p. 2503-2511, 2015.

SOUZA, M. T.; SILVA, M. D.; CARVALHO, R. Revisão integrativa: o que é e como fazer. Einstein, v. 8, n. 1, p. 102-106, 2010. DOI: https://doi.org/10.1590/s1679-45082010rw1134

WESTERMAN, G.; BONNET, D.; MCAFEE, A. Leading Digital: turning technology into business transformation. Boston: Harvard Business Review Press, 2014

Published

2026-03-02

How to Cite

ARTIFICIAL INTELLIGENCE AND EDUCATION: BETWEEN TECHNOLOGICAL INNOVATION AND ETHICAL CHALLENGES. (2026). REMUNOM, 13(01), 1-20. https://doi.org/10.66104/qkxnh791