Volume 10 Issue 3 (2021)

Artificial intelligence and education: A pedagogical challenge for the 21st century

pp. 7-12  |  Published Online: July 2021  |  DOI: 10.22521/edupij.2021.103.1

Esteban Vázquez-Cano

Abstract

Background/purpose – Education in the 21st century faces a series of challenges, including training in mobile and ubiquitous contexts, and the improvement of the didactic processes associated with online and face-to-face teaching. For this, teachers and students can and should take advantage of the potential of tools based on artificial intelligence.

Materials/methods – This study is a review article, which presents a brief literature review on the possible applications and functionalities of artificial intelligence in education.

Practical implications – One of the prominent emerging challenges in education consists of proposing models and propositions for the integration of artificial intelligence into teaching and learning processes, based on solid didactic and pedagogical principles. Meeting this challenge appropriately and effectively may help to create more flexible, personalized, and sustainable learning environments.

Conclusion – The integration of artificial intelligence within education should be approached from a strong pedagogical approach in which not only algorithms should converge, but also emotions and appropriate values.

Keywords: Artificial intelligence, education, didactics, pedagogy, sustainability.

References

Alenezi, H. S., & Faisal, M. H. (2020). Utilizing crowdsourcing and machine learning in education: Literature review. Education and Information Technologies, 25, 2971-2986. https://doi.org/10.1007/s10639-020-10102-w

Ball, R., Duhadway, L., Feuz, K., Jensen, J., Rague, B., & Weidman, D. (2019). Applying machine learning to improve curriculum design. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 787-793). ACM. https://doi.org/10.1145/3287324.3287430

Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), Article 832. https://doi.org/10.1145/3287324.3287430

Chen, G., Xu, B., Lu, M., & Chen, N.-S. (2018). Exploring blockchain technology and its potential applications for education. Smart Learning Environments, 5, Article 1 https://doi.org/10.1186/s40561-017-0050-x

Cohen, J. N. (2018). Exploring echo-systems: How algorithms shape immersive media environments. Journal of Media Literacy Education, 10(2), 139-151. https://doi.org/10.23860/JMLE-2018-10-2-8

Colace, F., Santo, M. D., Lombardi, M., Pascale, F., Pietrosanto, A., & Lemma, S. (2018). Chatbot for E-Learning: A Case of Study. International Journal of Mechanical Engineering and Robotics Research, 7(5), 528-533. https://doi.o0072g/10.18178/ijmerr.7.5.528-533

Cope, B., Kalantzis, M., & Searsmith, D. (2020). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies, Educational Philosophy and Theory. Advanced online publication. https://doi.org/10.1080/00131857.2020.1728732

Dewan, M. A. A., Murshed, M., & Lin, F. (2019). Engagement detection in online learning: A review. Smart Learning Environments, 6, Article 1. https://doi.org/10.1186/s40561-018-0080-z

Eurostat. (2021). Digital Economy and Society Database. https://ec.europa.eu/eurostat/web/digital-economy-and-society/data/database

Fryer, L. K., Nakao, K., & Thompson, A. (2019). Chatbot learning partners: Connecting learning experiences, interest and competence. Computers in Human Behavior, 93, 279-289. https://doi.org/10.1016/j.chb.2018.12.023

Lan, A. S., Waters, A. E., Studer, C., & Baraniuk, R. G. (2014). Sparse factor analysis for learning and content analytics. Journal of Machine Learning Research, 15, 1959-2008. https://www.jmlr.org/papers/v15/lan14a.html

McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27(4), 12-14. https://doi.org/10.1609/aimag.v27i4.1904

Reckase, M. (2009). Multidimensional item response theory. Springer.

Schmidt, J. P. (2015, October 27). Certificates, Reputation, and the Blockchain. MIT Media Lab. https://medium.com/mit-media-lab/certificates-reputation-and-the-blockchain-aee03622426f

SDG-Education 2030 Steering Committee Secretariat. (n.d.). Sustainable Development Goal 4 (SDG 4). UNESCO. https://sdg4education2030.org/the-goal

Vázquez‑Cano, E., Mengual‑Andrés, S., & López‑Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18, Article 33. https://doi.org/10.1186/s41239-021-00269-8

Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500-1509. https://doi.org/10.1177/0002764213479363

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