Impact of the use and level of adoption of artificial intelligence tools on the pedagogical effectiveness of teachers in secondary education: a systematic review (2020–2025)
DOI:
https://doi.org/10.71112/jf653j78Keywords:
artificial intelligence, technology adoption, teaching effectiveness, secondary education, systematic reviewAbstract
This systematic review synthesizes studies published between 2020 and 2025 on the relationship between teachers’ adoption of artificial intelligence tools and pedagogical effectiveness in secondary education. A total of 110 records were identified, 49 full texts were assessed, and 25 studies were included. Overall, findings suggest positive associations between AI use and improvements in lesson planning, learning personalization, feedback, and assessment, although effects are not consistent across contexts. Outcomes depend on teachers’ digital competence, targeted professional development, instructional intent, and institutional support. Recurrent barriers include algorithmic bias, privacy and data protection concerns, unequal access, increased workload, and limited policy guidance. The review concludes that AI can enhance teaching effectiveness when implemented as a pedagogical strategy supported by ethical governance and capacity building.
Downloads
References
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
Published
Issue
Section
License
Copyright (c) 2026 Andrés Alexander Melo Rodríguez (Autor/a)
This work is licensed under a Creative Commons Attribution 4.0 International License.
