Uso de la inteligencia artificial entre profesores de educación básica superior en Ecuador

Angélica Valeria Jimenez Banchon; Richard Ramirez-Anormaliza

doi:10.37431/conectividad.v5i3.148

Authors

Angélica Valeria Jimenez Banchon Universidad Península de Santa Elena https://orcid.org/0009-0006-1809-633X
Richard Ramirez-Anormaliza Universidad Estatal de Milagro, Universidad de Guayaquil https://orcid.org/0000-0003-0040-0381

DOI:

https://doi.org/10.37431/conectividad.v5i3.148

Keywords:

Artificial intelligence, Learning, Academic performance, Acceptance of technology

Abstract

This article examines the adoption of artificial intelligence (AI) among higher primary education teachers in Ecuador, highlighting the importance of intention to use, perceived usability, and expected usefulness. The need to improve usability and communicate the usefulness of AI for adoption is emphasized, with implications for teacher training. The study uses a quantitative approach and structural equation modeling to analyze data from 299 teachers from an education district, finding that perceived usability and expected usefulness significantly influence the intention to use AI. The findings suggest that improving usability and communicating the benefits of AI can encourage its adoption among educators. Topics include the evolution and future trends of AI in education, the integration of technology into teaching practices, and the teachers’ acceptance of technology through models such as the Technology Acceptance Model (TAM). The importance of equipping teachers with the skills necessary to integrate technology into education, inspired by frameworks such as TPACK, is also highlighted.

References

Ahn, Doyeon, & Kwangho, L. (2022). Analysis of achievement predictive factors and predictive AI model development - Focused on blended math classes. The Mathematical Education, 61(2), 257-271.

Al Rawashdeh, A. Z., Mohammed, E. Y., Al Arab, A. R., Alara, M., Al-Rawashdeh, B., & Al-Rawashdeh, B. (2021). Advantages and Disadvantages of Using e-Learning in University Education: Analyzing Students' Perspectives. Electronic Journal of E-Learning, 19(3), 107-117. https://doi.org/10.34190/ejel.19.3.2168

Alam, T. M., Mushtaq, M., Shaukat, K., Hameed, I. A., Sarwar, M. U., & Luo, S. (2021). A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models. APPLIED SCIENCES-BASEL, 11(19). https://doi.org/10.3390/app11199296

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411

Amjad, S., Younas, M., Anwar, M., Shaheen, Q., Shiraz, M., & Gani, A. (2022). Data Mining Techniques to Analyze the Impact of Social Media on Academic Performance of High School Students. WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022. https://doi.org/10.1155/2022/9299115

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T

Arango-Serna, M. D., Branch-Bedoya, J. W., & Jiménez-Builes, J. A. (2020). El Ágora U.S.B., 20(1), 190-209. https://doi.org/10.21500/16578031.4255

Backfisch, I., Lachner, A., Hische, C., Loose, F., & Scheiter, K. (2020). Professional knowledge or motivation? Investigating the role of teachers' expertise on the quality of technology-enhanced lesson plans. Learning and Instruction, 66, 101300. https://doi.org/10.1016/j.learninstruc.2019.101300

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1177/009207038801600107

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606. https://doi.org/10.1037/0033-2909.88.3.588

Bosch, N. (2021). Identifying supportive student factors for mindset interventions: A two-model machine learning approach. COMPUTERS & EDUCATION, 167. https://doi.org/10.1016/j.compedu.2021.104190

Botella Nicolás, A. M., & Ramos Ramos, P. (2019). Investigación-acción y aprendizaje basado en proyectos: Una revisión bibliográfica. Perfiles Educativos, 41(163), 109-122. https://doi.org/10.22201/iisue.24486167e.2019.163.58923

Cabero Almenara, J., & Llorente Cejudo, C. (2020). La adopción de las tecnologías por las personas mayores: Aportaciones desde el modelo TAM (Technology Acceptance Model). PUBLICACIONES, 50(1), 141-157. https://doi.org/10.30827/publicaciones.v50i1.8521

Chin, W. W. (1998). The Partial Least Squares Approach to Structural Equation Modeling. En Modern Methods for Business Research. Psychology Press.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). L. Erlbaum Associates.

Creswell, J. W., & Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008

Etikan, I., Musa, S. A., & Alkassim, R. S. (2015). Comparison of Convenience Sampling and Purposive Sampling. American Journal of Theoretical and Applied Statistics, 5(1), Article 1. https://doi.org/10.11648/j.ajtas.20160501.11

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312

Freitas, P. D. S. C. D., & Sousa, C. E. B. D. (2022). RELAÇÕES ENTRE A TEORIA DE PIAGET E A NEUROCIÊNCIA COGNITIVA NO ENSINO DE FÍSICA. https://doi.org/10.1590/SciELOPreprints.4378

García Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2023). La nueva realidad de la educación ante los avances de la inteligencia artificial generativa. RIED-Revista Iberoamericana de Educación a Distancia, 27(1), 9-39. https://doi.org/10.5944/ried.27.1.37716

Guerrero Salazar, C. V. (2022). Limitaciones del conectivismo en el Ecuador: Necesidades urgentes para la calidad. Revista Científica Ciencia y Tecnología, 22(33). https://doi.org/10.47189/rcct.v22i33.513

Gutierrez-Cirlos, C., Bermudez-Gonzalez, J. L., Carrillo-Perez, D. L., Hidrogo-Montemayor, I., Martinez-Gonzalez, A., Carrillo-Esper, R., & Sanchez-Mendiola, M. (2023). Medicine and the metaverse: Current applications and future. Gaceta medica de Mexico, 159(4), 280-286. https://doi.org/10.24875/GMM.M23000795

Hair, J. F. (Ed.). (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (Second edition). Sage.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118

ISO. (2018). ISO 9241-11:2018 Ergonomics of human-system interactionPart 11: Usability: Definitions and concepts. https://www.iso.org/standard/63500.html

Joo, L. H. (2020). A Systematic Review of Artificial Intelligence Use in English Learning: Focus on Higher Education. The Journal of Humanities and Social science, 11(6), 2027-2042. https://doi.org/10.22143/HSS21.11.6.143

Lozano Rodríguez, A., Zárate Ortiz, J. F., & Llaven Aguilar, M. I. (2018). Uso de Recursos Educativos en Línea en el nivel medio superior: Desarrollo de competencias didácticas del docente. CPU-e, Revista de Investigación Educativa, 26, 114-135. https://doi.org/10.25009/cpue.v0i26.2539

Maia, J. de S. Z., Bueno, A. P. A., & Sato, J. R. (2023). Applications of Artificial Intelligence Models in Educational Analytics and Decision Making: A Systematic Review. WORLD, 4(2), 288-313. https://doi.org/10.3390/world4020019

Melo, E. V. D., Mercado, L. P. L., & Melo, A. L. C. D. (2023). TECNOLOGIAS DIGITAIS E DESENVOLVIMENTO COGNITIVO NA APRENDIZAGEM DE MATEMÁTICA: UMA REVISÃO SISTEMÁTICA DA LITERATURA. https://doi.org/10.1590/SciELOPreprints.6762 https://doi.org/10.1590/SciELOPreprints.6762

Mijwil, M. M., & Abttan, R. A. (2021). Artificial Intelligence: A Survey on Evolution and Future Trends. Asian Journal of Applied Sciences, 9(2). https://doi.org/10.24203/ajas.v9i2.6589

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd Edition). McGraw-Hill. https://www.amazon.com/Psychometric-Theory-Jum-C-Nunnally/dp/007047849X

Ortega-Sánchez, D. (s. f.). ¿Cómo investigar en Didáctica de las Ciencias Sociales?

Pando, V. F. (2018). Tendencias didácticas de la educación virtual: Un enfoque interpretativo. Propósitos y Representaciones, 6(1). https://doi.org/10.20511/pyr2018.v6n1.167

Párraga Mendoza, C. E., & Arteaga Briones, L. A. (2023). Estándares de la UNESCO en el uso de las TIC en los docentes de la Unidad Educativa Atahualpa. Revista Científica Arbitrada Multidisciplinaria PENTACIENCIAS, 5(5), 507-519. https://doi.org/10.59169/pentaciencias.v5i5.761

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8

Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS (4.0) [Software]. https://www.smartpls.com/

Silva, J. B. D., Bilessimo, S. M. S., & Machado, L. R. (2021). INTEGRAÇÃO DE TECNOLOGIA NA EDUCAÇÃO: PROPOSTA DE MODELO PARA CAPACITAÇÃO DOCENTE INSPIRADA NO TPACK. Educação Em Revista, 37, e232757. https://doi.org/10.1590/0102-4698232757

Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009

Teo, T. (2010). A path analysis of pre-service teachers' attitudes to computer use: Applying and extending the technology acceptance model in an educational context. Interactive Learning Environments, 18(1). https://doi.org/10.1080/10494820802231327

Tigse-Carreño, C. (2019). El Constructivismo, según bases teóricas de César Coll. Revista Andina de Educación, 2(1), 25-28. https://doi.org/10.32719/26312816.2019.2.1.4

Topal, A. D., Eren, C. D., & Gecer, A. K. (2021). Chatbot application in a 5th grade science course. EDUCATION AND INFORMATION TECHNOLOGIES, 26(5), 6241-6265. https://doi.org/10.1007/s10639-021-10627-8

Tsai, S.-C., Chen, C.-H., Shiao, Y.-T., Ciou, J.-S., & Wu, T.-N. (2020). Precision education with statistical learning and deep learning: A case study in Taiwan. INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION, 17(1). https://doi.org/10.1186/s41239-020-00186-2

Universidad Estatal del Sur de Manabí, Cisneros Caicedo, A. J., Guevara García, A. F., Universidad Estatal del Sur de Manabí, Urdánigo Cedeño, J. J., Universidad Estatal del Sur de Manabí, Garcés Bravo, J. E., & Universidad Estatal del Sur de Manabí. (2022). Técnicas e Instrumentos para la Recolección de Datos que Apoyan a la Investigación Científica en Tiempo de Pandemia. Dominio de las Ciencias, 8(1), 1165-1185. https://doi.org/10.23857/dc.v8i1.2546

Varela-Ordorica, S. A., & Valenzuela-González, J. R. (2020). Uso de las tecnologías de la información y la comunicación como competencia transversal en la formación inicial de docentes. Revista Electrónica Educare, 24(1), 1-20. https://doi.org/10.15359/ree.24-1.10

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Wang, W., Nepal, S., Huckins, J. F., Hernandez, L., Vojdanovski, V., Mack, D., Plomp, J., Pillai, A., Obuchi, M., Dasilva, A., Murphy, E., Hedlund, E., Rogers, C., Meyer, M., & Campbell, A. (2022). First-Gen Lens: Assessing Mental Health of First-Generation Students across Their First Year at College Using Mobile Sensing. PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT, 6(2). https://doi.org/10.1145/3543194

Wu, W., & Plakhtii, A. (2021). E-Learning Based on Cloud Computing. International Journal of Emerging Technologies in Learning (iJET), 16(10), 4. https://doi.org/10.3991/ijet.v16i10.18579

Zawacki-Richter, O., Marín, V. I., Bond, M., & 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(1), 39. https://doi.org/10.1186/s41239-019-0171-0

Zuloaga Cachay, J. F., Carrión-Barco, G., Chayan Coloma, A., & Figueroa Piscoya, E. N. (2022). Gestión de equipamiento computacional en aulas de innovación pedagógica. Revista Venezolana de Gerencia, 27(28), 815-830. https://doi.org/10.52080/rvgluz.27.98.27