Vol. 8 (2024): Publicación continua
A) Teoría, filosofía, historia e investigación sobre la investigación

Artificial intelligence in education: Ethical considerations and the promotion of critical thinking

María José Nozato López
Sonora
Bio
Portada-v8i0

Published 2024-12-20

Keywords

  • Inteligencia artificial,
  • educación,
  • ética,
  • pensamiento crítico
  • Artificial intelligence,
  • education,
  • ethics,
  • critical thinking

How to Cite

Nozato López, . . . M. J. (2024). Artificial intelligence in education: Ethical considerations and the promotion of critical thinking. RECIE. Revista Electrónica Científica De Investigación Educativa, 8, e2357. https://doi.org/10.33010/recie.v8i0.2357

Abstract

This study conducts a systematic literature review on the use of artificial intelligence (AI) in education, focusing on ethical considerations and the promotion of critical thinking. Given the increasing use of AI tools in education, it is essential to reflect on the associated challenges and opportunities. UNESCO has established the need for ethical principles to ensure that these technologies respect human rights and contribute to well-being. The analysis addresses topics such as equity, privacy, transparency, and the role of AI in fostering critical thinking, as identified in UNESCO reports on the impact of AI on education. Through the review of existing research, best practices and recommendations are identified for institutions to implement ethical codes and policies that ensure the responsible use of AI, prioritizing students’ well-being and enhancing their analytical and questioning abilities. This aligns with UNESCO’s emphasis on the key role of educators in fostering critical thinking in the face of emerging technologies. The implementation of artificial intelligence in education requires not only access to these tools but also the capability of educators to guide students in their reflective and ethical use. Thus, educators play an essential role in promoting a critical approach that allows students to analyze and question the impact of these technologies on their learning and society.

References

  1. Boers, M. (2018). Graphics and statistics for cardiology: Designing effective tables for presentation and publication. Heart, 104(3), 192-200. https://doi.org/10.1136/heartjnl-2017-311581
  2. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (pp. 149-159).
  3. CASP [Critical Appraisal Skills Programme] (2018). CASP Checklists. https://casp-uk.net/casp-tools-checklists/
  4. Crawford, K., y Whittaker, M. (2019). AI now 2019 report. AI Now Institute. https://ainowinstitute.org/reports.html
  5. Decker, A., Peebles, E., y Cole, R. (2021). AI and ethics in educational contexts: Addressing algorithmic bias and accountability. AI and Ethics.
  6. Estonian Data Protection Inspectorate (2023). Guidelines for data protection in AI applications in education. https://www.aki.ee/en
  7. Estonian Government (2023). National strategy for artificial intelligence in education. Estonian Ministry of Education and Research. https://www.hm.ee/en
  8. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
  9. Floridi, L., y Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
  10. Holmes, W., Bialik, M., y Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  11. Luckin, R., Holmes, W., Griffiths, M., y Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
  12. Mayo-Wilson, E., Li, T., Fusco, N., y Dickersin, K. (2018). Practical guidance for using multiple data sources in systematic reviews and meta-analyses (with examples from the MUDS study). Research Synthesis Methods, 9(1), 2-12. https://doi.org/10.1002/jrsm.1277
  13. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., y Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716679679
  14. OECD [Organization for Economic Co-operation and Development] (2021). AI principles in education: Addressing equity and access in emerging technologies. OECD.
  15. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
  16. Perrenoud, P. (2010). Desarrollar la práctica reflexiva en el oficio de enseñar: profesionalización y razón pedagógica. Graó.
  17. Popenici, S. A., y Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12, 22. https://doi.org/10.1186/s41039-017-0062-8
  18. Stovold, E., Beecher, D., Foxlee, R., y Noel-Storr, A. (2014). Study flow diagrams in Cochrane systematic review updates: An adapted PRISMA flow diagram. Systematic Reviews, 3, 54. https://doi.org/10.1186/2046-4053-3-54
  19. UNESCO [Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura] (2022). Recomendación sobre la ética de la inteligencia artificial. https://unesdoc.unesco.org/ark:/48223/pf0000381137_spa
  20. Williamson, B. (2019). Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education. British Journal of Educational Technology, 50(6), 2794-2809. https://doi.org/10.1111/bjet.12849
  21. Zawacki-Richter, O., Marín, V. I., Bond, M., y Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0