AI-Enhanced EFL Teaching: Evidence from an Ecuadorian Public High School
Main Article Content
Abstract
This study examines EFL teachers' perceptions of artificial intelligence (AI) integration in a public secondary school in Santa Elena, Ecuador, employing a sequential explanatory mixed-methods design. Phase 1 consists of a systematic scoping review (PRISMA-ScR) synthesizing 21 peer-reviewed studies published between 2020 and 2025 on AI-enhanced EFL teaching, with emphasis on resource-constrained and Latin American contexts. Phase 2 reports descriptive findings from the EFL Teachers' Perceptions of AI Integration Survey (ETPAIS), a researcher-designed 24-item Likert instrument administered to all eight EFL teachers at the Unidad Educativa Otto Arosemena Gómez (N = 8; census sampling). Results reveal that teachers hold positive attitudes toward AI as a complementary pedagogical tool (D2: M = 4.13) but perceive institutional infrastructure as substantially inadequate for implementation (D3: M = 2.31). Professional development emerged as the most urgently demanded facilitating condition (Item 22: M = 4.63). These findings converge with international evidence regarding positive teacher conceptualizations of AI while diverging significantly regarding infrastructure sufficiency, confirming that adoption strategies derived from well-resourced contexts are not directly transferable to Ecuadorian public secondary education. Implications for educational policy and teacher training are discussed.
Downloads
Article Details
Section

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
References
Arce, C. M., Gavilanes, J. C., Arce, E. M., Haro, E. M., & Bonilla-Jurado, D. (2025). Artificial intelligence in higher education: Predictive analysis of attitudes and dependency among Ecuadorian university students. Sustainability, 17(17), 7741. https://doi.org/10.3390/su17177741 DOI: https://doi.org/10.3390/su17177741
Barredo-Ibáñez, D., De-la-Garza-Montemayor, D.-J., Torres-Toukoumidis, Á., & López-López, P.-C. (2021). Artificial intelligence, communication, and democracy in Latin America: A review of the cases of Colombia, Ecuador, and Mexico. Profesional de la Información, 30(6), e300616. https://doi.org/10.3145/epi.2021.nov.16 DOI: https://doi.org/10.3145/epi.2021.nov.16
Bauer, E., Greiff, S., Graesser, A. C., Scheiter, K., & Sailer, M. (2025). Looking beyond the hype: Understanding the effects of AI on learning. Educational Psychology Review, 37, 45. https://doi.org/10.1007/s10648-025-10020-8 DOI: https://doi.org/10.1007/s10648-025-10020-8
Bayaga, A. (2025). Leveraging AI-enhanced and emerging technologies for pedagogical innovations in higher education. Education and Information Technologies, 30, 1045–1072. https://doi.org/10.1007/s10639-024-13122-y DOI: https://doi.org/10.1007/s10639-024-13122-y
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications, Inc. https://edge.sagepub.com/creswellrd5e
Cruz, G., Riobó, A., Pfeifer, M., & Duarte, D. (2024). AI from the ground up: Challenges and opportunities in the context of Latin America and the Caribbean. Inter-American Development Bank. https://doi.org/10.18235/0013275 DOI: https://doi.org/10.18235/0013275
De La Torre, A., & Baldeon-Calisto, M. (2024). Generative artificial intelligence in Latin American higher education: A systematic literature review. En A. Varol, M. Karabatak, C. Varol, & E. Tuba (Eds.), 12th International Symposium on Digital Forensics and Security, ISDFS 2024 (pp. 1–7). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ISDFS60797.2024.10527283 DOI: https://doi.org/10.1109/ISDFS60797.2024.10527283
Derinalp, P., & Halife, M. (2025). Pre-service English as a foreign language teachers’ attitudes toward artificial intelligence. Journal of Theoretical Educational Sciences, 18(3), 609–629. https://doi.org/10.30831/akukeg.1644354 DOI: https://doi.org/10.30831/akukeg.1644354
Leavy, P. (2022). Research design: Quantitative, qualitative, mixed methods, arts-based, and community-based participatory research approaches (2nd ed.). Guilford Press.
Okoye, K., Hussein, H., Arrona-Palacios, A., Quintero, H. N., Peña Ortega, L. O., Lopez Sanchez, A., Arias Ortiz, E., Escamilla, J., & Hosseini, S. (2023). Impact of digital technologies upon teaching and learning in higher education in Latin America: An outlook on the reach, barriers, and bottlenecks. Education and Information Technologies, 28(2), 2291–2360. https://doi.org/10.1007/s10639-022-11214-1 DOI: https://doi.org/10.1007/s10639-022-11214-1
Pérez-Campdesuñer, R., Sánchez-Rodríguez, A., García-Vidal, G., Martínez-Vivar, R., & De Miguel-Guzmán, M. (2025). Artificial intelligence in Ecuadorian SMEs: Drivers and obstacles to adoption. Information, 16(6), 443. https://doi.org/10.3390/info16060443 DOI: https://doi.org/10.3390/info16060443
Richards, J. C., & Renandya, W. A. (Eds.). (2002). Methodology in language teaching: An anthology of current practice. Cambridge University Press. https://assets.cambridge.org/97805218/08293/frontmatter/9780521808293_frontmatter.pdf DOI: https://doi.org/10.1017/CBO9780511667190
Riggs, V. (2025). Teachers' perceptions and readiness for AI integration in under-resourced K-12 classrooms. Journal of Research Initiatives, 9(1), Article 1. https://digitalcommons.uncfsu.edu/jri/vol9/iss1/1
Salas-Pilco, S. Z., & Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: A systematic review. International Journal of Educational Technology in Higher Education, 19(1), 21. https://doi.org/10.1186/s41239-022-00326-w DOI: https://doi.org/10.1186/s41239-022-00326-w
Shafiee Rad, H. (2025). Reinforcing L2 reading comprehension through artificial intelligence intervention: Refining engagement to foster self-regulated learning. Smart Learning Environments, 12, 23. https://doi.org/10.1186/s40561-025-00377-2 DOI: https://doi.org/10.1186/s40561-025-00377-2
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., ... Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850 DOI: https://doi.org/10.7326/M18-0850
Winder, G., Bass, S., Schiele, D., & Buchner, J. (2024). Using large language models for content creation impacts online learning evaluation outcomes. International Journal on E-Learning, 23(3), 305–318. https://doi.org/10.70725/423664moqcrd DOI: https://doi.org/10.70725/423664moqcrd