Impacto del uso y del nivel de adopción de herramientas de inteligencia artificial en la eficacia pedagógica de los docentes de educación secundaria: una revisión sistemática (2020–2025)
DOI:
https://doi.org/10.71112/jf653j78Palavras-chave:
inteligencia artificial;, adopción tecnológica;, eficacia docente;, educación secundaria;, revisión sistemáticaResumo
Esta revisión sistemática sintetiza evidencia publicada entre 2020 y 2025 sobre la relación entre la adopción de herramientas de inteligencia artificial y la eficacia pedagógica docente en educación secundaria. Se identificaron 110 registros, se evaluaron 49 textos completos y se incluyeron 25 estudios. En conjunto, los hallazgos señalan asociaciones positivas entre el uso de IA y mejoras en planificación, personalización del aprendizaje, retroalimentación y evaluación, aunque los efectos no son uniformes. La influencia depende de la competencia digital docente, la formación específica, la intención pedagógica y el apoyo institucional. Se reportan barreras recurrentes como sesgos algorítmicos, riesgos de privacidad, brechas de acceso, aumento de carga laboral y ausencia de lineamientos claros. Se concluye que la IA potencia la eficacia docente cuando se integra con criterios didácticos y gobernanza ética.
Downloads
Referências
Al-khresheh, M. H. (2024). Bridging technology and pedagogy from a global lens: Teachers’ perspectives on integrating ChatGPT in English language teaching. Computers and Education: Artificial Intelligence, 6, 100218. https://doi.org/10.1016/j.caeai.2024.100218
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 100099. https://doi.org/10.1016/j.caeai.2022.100099
Bergdahl, N., & Sjöberg, J. (2025). Attitudes, perceptions and AI self-efficacy in K-12 education. Computers and Education: Artificial Intelligence, 8, 100358. https://doi.org/10.1016/j.caeai.2024.100358
Cabero-Almenara, J., Palacios-Rodríguez, A., Loaiza-Aguirre, M. I., & Rivas-Manzano, M. D. R. (2024). Acceptance of educational artificial intelligence by teachers and its relationship with some variables and pedagogical beliefs. Education Sciences, 14(7), 740. https://doi.org/10.3390/educsci14070740
Cheah, Y. H., Lu, J., & Kim, J. (2025). Integrating generative artificial intelligence in K-12 education: Examining teachers’ preparedness, practices, and barriers. Computers and Education: Artificial Intelligence, 8, 100363. https://doi.org/10.1016/j.caeai.2025.100363
Collie, R. J., & Martin, A. J. (2024). Teachers’ motivation and engagement to harness generative artificial intelligence for teaching and learning: The role of contextual, occupational, and background factors. Computers and Education: Artificial Intelligence, 6, 100224. https://doi.org/10.1016/j.caeai.2024.100224
Dalyanci, A. A., Mast, L., Krushinskaia, K., & Raes, A. (2025). Detecting innovators in the field: Teachers’ perceptions and adoption of generative AI in education. The Open Technology in Education, Society, and Scholarship Association Journal, 5(1), 1–50. https://doi.org/10.18357/otessaj.2025.5.1.89
Demszky, D., Liu, J., Hill, H. C., Sanghi, S., & Chung, A. (2025). Automated feedback improves teachers’ questioning quality in brick-and-mortar classrooms: Opportunities for further enhancement. Computers & Education, 227, 105183. https://doi.org/10.1016/j.compedu.2024.105183
Estrada-Araoz, E. G., Quispe-Aquise, J., Malaga-Yllpa, Y., Larico-Uchamaco, G. R., Pizarro-Osorio, G. R., Mendoza-Zuñiga, M., Velasquez-Bernal, A. C., Roque-Guizada, C. E., & Huamaní-Pérez, M. I. (2024). Role of artificial intelligence in education: Perspectives of Peruvian basic education teachers. Data and Metadata, 3, 325. https://doi.org/10.56294/dm2024325
Granström, M., & Oppi, P. (2025). Assessing teachers’ readiness and perceived usefulness of AI in education: An Estonian perspective. Frontiers in Education, 10, 1622240. https://doi.org/10.3389/feduc.2025.1622240
Guan, L., Zhang, Y., & Gu, M. M. (2025). Pre-service teachers preparedness for AI-integrated education: An investigation from perceptions, capabilities, and teachers’ identity changes. Computers and Education: Artificial Intelligence, 8, 100341. https://doi.org/10.1016/j.caeai.2024.100341
Kim, N. J., & Kim, M. K. (2022). Teacher’s perceptions of using an artificial intelligence-based educational tool for scientific writing. Frontiers in Education, 7, 755914. https://doi.org/10.3389/feduc.2022.755914
Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, 100328. https://doi.org/10.1016/j.caeai.2024.100328
Leibur, T., & Saks, K. (2025). Leveraging learning analytics to support teachers’ professional development: Insights from a digital application. Frontiers in Education, 10, 1639217. https://doi.org/10.3389/feduc.2025.1639217
Liu, Y., Wang, Q., & Lei, J. (2025). Adopting generative AI in future classrooms: A study of preservice teachers’ intentions and influencing factors. Behavioral Sciences, 15(8), 1040. https://doi.org/10.3390/bs15081040
Lu, H., He, L., Yu, H., Pan, T., & Fu, K. (2024). A study on teachers’ willingness to use generative AI technology and its influencing factors: Based on an integrated model. Sustainability, 16(16), 7216. https://doi.org/10.3390/su16167216
Moorhouse, B. L. (2024). Beginning and first-year language teachers’ readiness for the generative AI age. Computers and Education: Artificial Intelligence, 6, 100201. https://doi.org/10.1016/j.caeai.2024.100201
Navío-Inglés, M., Guzmán Mora, J., O’Connor-Jiménez, P., & García González, A. (2025). What’s next for feedback in writing instruction? Pre-service teachers’ perceptions of assessment practices and the role of generative AI. Education Sciences, 15(11), 1534. https://doi.org/10.3390/educsci15111534
Ofem, U. J., Orim, F. S., Edam-Agbor, I. B., Amanso, E. O. I., Eni, E., Ukatu, J. O., Ovat, S. V., Osang, A. W., Dien, C., & Abuo, C. B. (2025). Teachers’ preparedness for the utilization of artificial intelligence in classroom assessment: The contributory effects of attitude toward technology, technological readiness, and pedagogical beliefs with perceived ease of use and perceived usefulness as mediators. Frontiers in Education, 10, 1568306. https://doi.org/10.3389/feduc.2025.1568306
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Peikos, G., & Stavrou, D. (2025). ChatGPT for science lesson planning: An exploratory study based on pedagogical content knowledge. Education Sciences, 15(3), 338. https://doi.org/10.3390/educsci15030338
Prilop, C. N., Mah, D.-K., Jacobsen, L. J., Hansen, R. R., Weber, K. E., & Hoya, F. (2025). Generative AI in teacher education: Educators’ perceptions of transformative potentials and the triadic nature of AI literacy explored through AI-enhanced methods. Computers and Education: Artificial Intelligence, 9, 100471. https://doi.org/10.1016/j.caeai.2025.100471
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693
UNESCO. (2024). AI competency framework for teachers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391104
Uwosomah, E. E., & Dooly, M. (2025). It is not the huge enemy: Preservice teachers’ evolving perspectives on AI. Education Sciences, 15(2), 152. https://doi.org/10.3390/educsci15020152
van Leeuwen, A., Strauß, S., & Rummel, N. (2023). Participatory design of teacher dashboards: Navigating the tension between teacher input and theories on teacher professional vision. Frontiers in Artificial Intelligence, 6, 1039739. https://doi.org/10.3389/frai.2023.1039739
Wang, K., Ruan, Q., Zhang, X., Fu, C., & Duan, B. (2024). Pre-service teachers’ GenAI anxiety, technology self-efficacy, and TPACK: Their structural relations with behavioral intention to design GenAI-assisted teaching. Behavioral Sciences, 14(5), 373. https://doi.org/10.3390/bs14050373
Wilson, J., Delgado, A., Palermo, C., Cruz Cordero, T. M., Myers, M. C., Eacker, H., Potter, A., Coles, J., & Zhang, S. (2024). Middle school teachers’ implementation and perceptions of automated writing evaluation. Computers & Education Open, 7, 100231. https://doi.org/10.1016/j.caeo.2024.100231
Downloads
Publicado
Edição
Seção
Licença
Copyright (c) 2026 Andrés Alexander Melo Rodríguez (Autor/a)

Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.






