El Feedback Basado en Inteligencia Artificial en el Aprendizaje de Estudiantes de Educación General Básica Superior: Percepción Docente

Carlos Daniel Velásquez-García; Gladys Nohelia Villegas-Bravo; Emely Jamilet Cárdenas-Posligua

doi:10.70171/g3bjqr97

Authors

Carlos Daniel Velásquez-García Universidad Técnica Estatal de Quevedo Author https://orcid.org/0009-0000-4383-9736
Gladys Nohelia Villegas-Bravo Universidad Técnica Estatal de Quevedo Author https://orcid.org/0000-0002-3819-7246
Emely Jamilet Cárdenas-Posligua Universidad Técnica Estatal de Quevedo Author https://orcid.org/0000-0001-8682-4147

DOI:

https://doi.org/10.70171/g3bjqr97

Keywords:

: active learning, artificial intelligence, feedback, educational technology

Abstract

Justification: The incorporation of generative tools in school settings raises questions about their contributions, forms of use, and potential effects on educational processes. Objective: Therefore, this study analyzed teachers’ perceptions of the impact of artificial intelligence-based feedback on the learning of Upper Basic General Education students. Methodology: A quantitative approach was employed, with a descriptive, non-experimental design. Thirty Upper Basic Education teachers from “El Empalme” Educational Unit participated. A survey was used as the data collection technique, and an online questionnaire was used as the instrument to address aspects related to teachers’ use of AI. Results: Of the teachers, 55.6% use ChatGPT for feedback, 50% frequently use AI, and 66.7% apply it during the teaching process. Additionally, 77.8% reported an intermediate level of proficiency, while 72.2% engage in self-directed training. Furthermore, 66.7% perceived greater autonomy, 72.2% perceived a slight increase in motivation, and 72.2% perceived greater dependence on information searching. Conclusion: AI-based feedback can strengthen the learning of Upper Basic Education students when used responsibly and as a complement to teacher guidance.

Downloads

Download data is not yet available.

References

Brummer, L., de Boer, H., Mouw, J., y Strijbos, J. (2024). A meta-analysis of the effects of context, content, and task factors of digitally delivered instructional feedback on learning performance. Learning Environments Research, 27(3), 453–476. https://doi.org/10.1007/s10984-024-09501-4

Castillo, M. (2023). Impacto de la inteligencia artificial en el proceso de enseñanza y aprendizaje en la educación secundaria. LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades, 4(6), 515–530. https://doi.org/10.56712/latam.v4i6.1459

Crosthwaite, P., y Sun, S. (2026). Generative AI and L2 written feedback studies: A scoping review. RELC Journal, 57(1), 207-219. https://journals.sagepub.com/doi/10.1177/00336882251386530

Díaz, J., Molina, R., Bayas, C., y Ruiz, A. (2024). Asistencia de la inteligencia artificial generativa como herramienta pedagógica en la educación superior. Revista de Investigación en Tecnologías de la Información, 12(26), 61–76. https://doi.org/10.36825/RITI.12.26.006

Fernández, M., Román, D., Jurado, A., Limón, D., y Torres, C. (2024). Artificial intelligence in Latin American universities: Emerging challenges. Computación y Sistemas, 28(2), 435–450. https://doi.org/10.13053/CyS-28-2-4822

Fidalgo-Blanco, A., Fonseca-Escudero, D., y Sein-Echaluce, M. (2026). Un modelo integrado basado en IA generativa para la personalización del aprendizaje y la retroalimentación formativa. RIED-Revista Iberoamericana de Educación a Distancia, 29(2), 209–233. https://doi.org/10.5944/ried.47210

Hernández-Sampieri, R., y Mendoza, C. (2018). Metodología de la investigación: Las rutas cuantitativa, cualitativa y mixta. McGraw-Hill Education

Huamán, J., Treviños, L., y Medina, W. (2022). Epistemología de las investigaciones cuantitativas y cualitativas. Horizonte de la Ciencia, 12(22), 27–47. https://www.redalyc.org/journal/5709/570971314003/html/

Játiva-Ávila, D. J., Llumiquinga-Loya, R. del P., Gualotuña-Quishpe, M. N., y Poma-Ortiz, P. de los A. (2025). Aplicación de la inteligencia artificial en la retroalimentación educativa: oportunidades y retos en el aula digital. RICEd: Revista de Investigación en Ciencias de la Educación, 3(6), 178–189. https://doi.org/10.53877/phj9w436

Jiménez-García, E., Ruiz-Lázaro, J., Martínez-Requejo, S., y Redondo-Duarte, S. (2025). Inteligencia Artificial y chatbots para una educación superior sostenible: una revisión sistemática. RIED-Revista Iberoamericana de Educación a Distancia, 28(2), 81–104. https://doi.org/10.5944/ried.28.2.43240

Lo, C. K., Hew, K. F., y Jong, M. S.-Y. (2024). The influence of ChatGPT on student engagement: A systematic review and future research agenda. Computers & Education, 219, 105100. https://doi.org/10.1016/j.compedu.2024.105100

Lozano, M. (2025). El profesorado y el uso de la inteligencia artificial (IA) como proceso de aprendizaje. Revista de Investigación en Tecnologías de la Información, 13(30), 1–8. https://doi.org/10.36825/RITI.13.30.001

Mejía-Rivas, J. (2022). Los paradigmas en la investigación científica. Revista Ciencia Agraria, 1(14), 7-14. https://doi.org/10.35622/j.rca.2022.03.001

Ng, D., Tan, C., y Leung, J. (2024). Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55, 1328–1353. https://doi.org/10.1111/bjet.13454

Nicol, D. y Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090

Ossa, C., y Willatt, C. (2023). Uso de Inteligencia Artificial Generativa para retroalimentar escritura académica en procesos de formación inicial docente. European Journal of Education and Psychology, 16(2), 1–16. https://doi.org/10.32457/ejep.v16i2.2412

Santos-Vesga, A., Cáceres-Castellanos, G., y Ballesteros-Ricaurte, J. (2024). Rol de la inteligencia artificial en educación básica: Un mapeo sistémico global. Revista Latinoamericana de Estudios Educativos, 20(2), 47–72. https://doi.org/10.17151/rlee.2024.20.2.3

Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18, 119–144. https://doi.org/10.1007/BF00117714

Xia, Q., Weng, X., Ouyang, F., Lin, T., y Chiu, T. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(1), 40. https://doi.org/10.1186/s41239-024-00468-z

Yue, M., Jong, M., y Ng, D. (2024). Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Education and Information Technologies, 29, 19505–19536. https://doi.org/10.1007/s10639-024-12621-2

Zhai, C., Wibowo, S., y Li, L. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart Learning Environments, 11, 28. https://doi.org/10.1186/s40561-024-00316-7