The European Educational Researcher

Attention to diversity from artificial intelligence

The European Educational Researcher, Volume 6, Issue 3, October 2023, pp. 101-115
OPEN ACCESS VIEWS: 3289 DOWNLOADS: 253 Publication date: 15 Oct 2023
ABSTRACT
Artificial intelligence (AI) is influencing various sectors of society, including the educational field. The use of AI can have great potential in education, however, it is necessary to know both its performance and its limitations. The main objective of this study is to analyze the prompts made by teachers in initial training in relation to the topic of specific educational support needs, classifying them according to Bloom's Taxonomy. For this, 63 students from the first year of the Primary Education Degree in the subject Information and Communication Technology applied to Education participated. The results show that the highest frequency of prompts made by students correspond to the highest levels of Bloom's taxonomy (apply and create), which suggests that students are capable of using the knowledge acquired in the subject to create new learning situations with their future students. This confirms that the implementation of this methodology is beneficial for the development of cognitive and pedagogical skills of future teachers.
KEYWORDS
Artificial intelligence, Bloom’s Taxonomy, higher education, special education
CITATION (APA)
Domínguez-González, M. D. L. Á., Hervás-Gómez, C., Díaz-Noguera, M. D., & Reina-Parrado, M. (2023). Attention to diversity from artificial intelligence. The European Educational Researcher, 6(3), 101-115. https://doi.org/10.31757/euer.633
REFERENCES
  1. Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's Taxonomy of Educational Objectives. Longman.
  2. Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., ... & Wittrock, M. C. (Eds.) (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Addison Wesley Longman.
  3. Biswas, P., Sameem, M., & Mallick, L. (2023). Role of artificial intelligence in digital transformation of education. Journal of Data Acquisition and Processing, 38(2), 985-989. https://doi.org/10.5281/zenodo.776668
  4. ChatGPT. https://chat.openai.com/
  5. Daniel, K.L., & Darragh, L. (2018). Revisiting Bloom’s Taxonomy for Ethics and Other Educational Domains. Ethics and Behavior, 28(4), 337-356.
  6. Larsen, T. M., Endo, B. H., Yee, A. T., Do, T., & Lo, S. M. (2022). Probing Internal Assumptions of the Revised Bloom's Taxonomy. CBE life sciences education, 21(4), ar66. https://doi.org/10.1187/cbe.20-08-0170
  7. Fergus, S., Botha, M., & Ostovar, M. (2023). Evaluating Academic Answers Generated Using ChatGPT. Journal of Chemical Education, 100(4), 1672-1675. https://doi.org/10.1021/acs.jchemed.3c00087
  8. Fisher, D., & Frey, N. (2018). Rigor in Your Classroom: A Toolkit for Teachers. Routledge.
  9. Friend, M. (2014). Special Education: Contemporary Perspectives for School Professionals. Pearson.
  10. Fuentes Gutiérrez, V., García-Domingo, M., Amezcua Aguilar, P., & Amezcua Aguilar, T. (2021). Attention to the functional diversity on primary education. REICE. Revista Iberoamericana Sobre Calidad, Eficacia y Cambio En Educación, 19(1), 91-106. https://doi.org/10.15366/REICE2021.19.1.006
  11. Green, L.S. (2019). Digital Technology Integration and Bloom's Revised Taxonomy: A Literature Review. International Journal of Education and Learning, 1(1), 1-13.
  12. Hattie, J., (2016). Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. Routledge.
  13. Hernández-Sampieri, R., Fernández-Collado, C., & Baptista-Lucio, P. (2014). Metodología de la investigación [Research methodology]. McGraw-Hill Education.
  14. Jamal, Afiya. (2023). The Role of Artificial Intelligence (AI) in Teacher Education: Opportunities & Challenges. International Journal of Research and Analytical Reviews, 10(1), 140-146. https://ijrar.org/papers/IJRAR23A2629.pdf
  15. Kerrigan, J., Cochran, G., Tabanli, S., Charnley, M., & Mulvey, S. (2022). Post-COVID changes to assessment practices: A case study of undergraduate STEM recitations. Journal of Educational Technology Systems, 51(2), 192-201. https://doi.org/10.1177/00472395221118392
  16. López Galisteo, A. J., Rodríguez Calzada, L., & Montes Diez, R. (2023). Guía de uso de ChatGPT para potenciar el aprendizaje activo e interactivo en el aula universitaria [Guide to using ChatGPT to enhance active and interactive learning in the university classroom]. https://hdl.handle.net/10115/22149
  17. Lopezosa, C., Codina, L., Ferran-Ferrer, N. (2023). ChatGPT como apoyo a las systematic scoping reviews: integrando la inteligencia artificial con el framework SALSA [ChatGPT as a support for systematic scoping reviews: integrating artificial intelligence with the SALSA framework.]. Collecció del CRICC. Universitat de Barcelona.
  18. Lorente, J. C. C. (2023). Percepción docente del trabajo educativo interdisciplinar como vía hacia una educación inclusiva [Teachers' perceptions of interdisciplinary educational work as a pathway to inclusive education]. Revista de educación, innovación y formación: REIF, 8, 69-88.
  19. Naidu, K., & Sevnarayan, K. (2023). ChatGPT: An ever-increasing encroachment of artificial intelligence in online assessment in distance education. Online Journal of Communication and Media Technologies, 13(1), e2023xx. https://doi.org/10.30935/ojcmt/13291
  20. Nipun, M.S., Talukder, M.H., Butt, U.J., Sulaiman, R.B. (2023). Influ-ence of Artificial Intelligence in Higher Education; Impact, Risk and Counter Measure. In H. Jahankhani, A. Jamal, G. Brown, E. Sainidis, R. Fong, U.J. Butt (Eds). AI, Blockchain and Self-Sovereign Identity in Higher Education. Advanced Sciences and Technologies for Security Applications. Springer. https://doi.org/10.1007/978-3-031-33627-0_7
  21. Oaks, M.M., (2017). Bloom's Taxonomy and Its Use in Classroom Assessment. Journal of Applied Learning and Teaching, 1(1), 57-62.
  22. Ortiz González, M. del C. (2023). Hacia una educación inclusiva. La Educación Especial ayer, hoy y mañana [Towards inclusive education. Special Education yesterday, today and tomorrow]. Siglo Cero, 54(1), 11–24. https://doi.org/10.14201/scero202354125096
  23. Prince, M. (2004). Does Active Learning Work? A Review of the Research. Journal of Engineering Education, 93(3), 223–231.
  24. Shrivastava, R. (2023). Role of Artificial Intelligence in Future of Education. International Journal of Professional Business Review, 8(1), e0840. https://doi.org/10.26668/businessreview/2023.v8i1.840
  25. Tomlinson, C. A. (2014). The Differentiated Classroom: Responding to the Needs of All Learners. Association for Supervision and Curriculum Development.
  26. Valdés, R., Jiménez, L., & Jiménez, F. (2022). Radiografía de la investigación sobre educación inclusiva [Inclusive education research radiography]. Cuadernos de Pesquisa, 52. https://doi.org/10.1590/198053149524
  27. Vos, P., & Frejd, P. (2022). Grade 8 students appropriating sankey diagrams: the first cycle in an educational design research. Journal on Mathematics Education, 13(2), 289-306. https://doi.org/10.22342/jme.v13i2.pp289-306
  28. Xiao, Y., & Zhi, Y. (2023). An Exploratory Study of EFL Learners’ Use of ChatGPT for Language Learning Tasks: Experience and Perceptions. Languages, 8(3), 212. https://doi.org/10.3390/languages8030212
LICENSE
Creative Commons License