AI in the Classroom: A Systematic Review of Barriers to Educator Acceptance

Authors

  • Ellaine Joy Guyo Eusebio
  • Philip Baldera
  • Aljay Marc Patiam
  • Emelyn Rico Villanueva
  • Norilyn Asedente Gaa
  • Anna Marie Faderon Solis
  • Ma. Loresa C. Soriano
  • Alvin L. Ribon

Keywords:

Artificial Intelligence; Educator Acceptance; Educational Technology Integration; PRISMA

Abstract

This study investigates the barriers to educator acceptance of Artificial Intelligence (AI) technologies in education through a systematic review guided by the PRISMA 2020 framework. With educators occupying a pivotal role in the classroom as facilitators of learning and mediators of technology use, their acceptance and integration of AI tools are critical to the success of educational innovation. Educators' readiness and resistance to AI are examined through the synthesis of empirical findings from peer-reviewed studies published between 2020 and 2025. From an initial 404 records identified, 310 remained after duplicate removal. Following title and abstract screening, 33 records were retained. After a full-text eligibility review, 14 studies were included in the qualitative synthesis, of which 10 met the criteria for final analysis. The results highlight that demographic factors such as age, gender, and digital literacy significantly affect educators' readiness to use AI. Common barriers include insufficient training, infrastructure limitations, ethical concerns, anxiety, and perceived misalignment between AI tools and pedagogical goals. Barriers vary by regional and institutional context. Developing countries face technological and resource-based challenges, while developed nations encounter pedagogical and ethical issues. The study compares several theoretical models, including the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), to explain variations in AI adoption, further integrating perspectives on emotional response, professional identity, and institutional culture. This review provides critical insights for educational policymakers, leaders, and technology developers to design inclusive, ethically sound, and pedagogically aligned strategies for AI integration in classrooms.

https://doi.org/10.26803/ijlter.24.9.7

References

Abdelaal, N. M., & Al Sawi, I. (2024). Perceptions, challenges, and prospects: University professors’ use of artificial intelligence in education. Australian Journal of Applied Linguistics, 7(1), Article 1309. https://doi.org/10.29140/ajal.v7n1.1309

Adams, J., & Thompson, R. (2022). Predictors of educator AI self-efficacy: A meta-analytic path analysis. Computers in Human Behavior, 128, 107118. https://doi.org/10.1016/j.chb.2021.107118

Adams, R. C., & Zhang, J. (2023). Factors influencing K–12 teachers’ adoption of AI tools in classrooms. Journal of Educational Technology Systems, 51(3), 285–307. https://doi.org/10.1177/00472395221145678

Adiguzel, T., Kaya, T., & Cansu, A. (2023). Artificial intelligence applications in education: Opportunities and challenges. Education and Information Technologies, 28, 317–334. https://doi.org/10.1007/s10639-022-11247-1

Ahmed, S., Khalil, I., Chowdhury, B., Haque, R., Senathirajah, A. R., & Din, F. M. O. (2022). Motivators and barriers of artificial intelligence (AI) based teaching. Eurasian Journal of Educational Research, 100, 74–89. https://doi.org/10.14689/ejer.2022.100.4

Al Darayseh, A. (2023). Teachers’ attitudes toward artificial intelligence in education: A qualitative case study. Education and Information Technologies, 28, 613–629. https://doi.org/10.1007/s10639-023-11756-0

Alshammari, S., Ahmad, M., & Akour, M. (2024). Educators’ readiness for AI adoption: A cross-sectional study of barriers and enablers. Journal of Educational Computing Research. Advance online publication. https://doi.org/10.1177/0735633124123456

Alshorman, S. (2024). The readiness to use AI in teaching science: Science teachers’ perspective. Journal of Baltic Science Education, 23(3), 432–445. https://doi.org/10.33225/jbse/24.23.432

Anderson, M., & Thompson, J. (2022). Preparing educators for AI integration: Professional development and policy implications. Educational Technology Research and Development, 70(6), 1901–1924. https://doi.org/10.1007/s11423-021-10020-6

Ayanwale, M. A., Adelana, O. P., & Odufuwa, T. T. (2024). Exploring STEAM teachers’ trust in AI based educational technologies: A structural equation modelling approach. International Journal of Learning, Teaching and Educational Research, 23(6), 1–19. https://doi.org/10.1007/s44217-024-00092-z

Brown, L., & Garcia, T. (2023). A meta-analysis of educator concerns AI ethics and privacy in educational settings. British Journal of Educational Technology, 54(4), 1117–1142. https://doi.org/10.1111/bjet.13284

Brown, S., & Yupse, R. (2024). Teachers' digital literacy and AI competency: A 21st-century framework. Educational Technology & Society, 27(1), 25–41. https://doi.org/10.1023/A:1016088205679

Chananagari, S., & Prabhakar, R. (2024). Barriers to AI adoption in higher education institutions: A systematic review. Educational Research Review, 42, 100544. https://doi.org/10.1016/j.edurev.2024.100544

Chen, H., & Thompson, K. L. (2022). Educator resistance to artificial intelligence: A systematic review and meta-analysis. Computers & Education, 179, 104429. https://doi.org/10.1016/j.compedu.2022.104429

Cheng, E. C. K., & Wang, T. (2023). Leading digital transformation and eliminating barriers for teachers to incorporate artificial intelligence in basic education in Hong Kong. Computers and Education: Artificial Intelligence, 5, Article 100171. https://doi.org/10.1016/j.caeai.2023.100171

Daskalaki, E., Psaroudaki, K., & Fragopoulou, P. (2024). Navigating the future of education: Educators’ insights on AI integration and challenges in Greece, Hungary, Latvia, Ireland and Armenia. CoRR, abs/2408.15686. https://doi.org/10.48550/arXiv.2408.15686

Fernández Miranda, M., Acosta, D. R., Jurado Rosas, A., Limón Domínguez, D., & Torres, C. F. (2024). Artificial intelligence in Latin American universities: Emerging challenges. Computación y Sistemas, 28(2), 435. http://dx.doi.org/10.13053/cys-28-2-4822

García-Martínez, I., Ruiz, D. M., & Soto, A. (2023). Teachers and AI: Examining trust, readiness, and ethics in educational technology adoption. British Journal of Educational Technology, 54(3), 789–805. https://doi.org/10.1111/bjet.13235

Garcia, P., & Williams, J. (2023). Comparing AI acceptance models in education: A meta-analytic structural equation modeling approach. Computers & Education, 189, 104688. https://doi.org/10.1016/j.compedu.2023.104688

Giannini, S. (2024). Shaping the future of education with AI: UNESCO’s roadmap. UNESCO Education Report Series. https://unesdoc.unesco.org

Harris, P., & Wong, L. (2023). Demographic factors influencing educator acceptance of AI: A meta-regression analysis. Educational Technology Research and Development, 71(2), 719–743. https://doi.org/10.1007/s11423-022-10166-0

Jackson, M., & Campbell, T. (2022). Institutional factors affecting AI implementation in education: A meta-analysis of organizational studies. Educational Administration Quarterly, 58(3), 417–451. https://doi.org/10.1177/0013161X221094567

Kaya, T., Gök, M., & Yilmaz, H. (2022). The role of personality traits in AI technology acceptance among educators. Computers in Human Behavior, 135, 107377. https://doi.org/10.1016/j.chb.2022.107377

Lee, J., & Martinez, S. (2023). A meta-analytic review of cultural differences in educator acceptance of AI technologies. Educational Technology Research and Development, 71(4), 1845–1872. https://doi.org/10.1007/s11423-023-10174-w

Li, W., & Thomas, J. (2023). Educator acceptance of AI across disciplines: A meta-analysis of disciplinary differences. Higher Education, 86(4), 631–652. https://doi.org/10.1007/s10734-023-00898-2

Martinez, K., & Wilson, P. (2022). Trust as mediator of AI acceptance in education: A meta-analytic structural equation model. Educational Technology Research and Development, 70(5), 2147–2173. https://doi.org/10.1007/s11423-022-10112-0

Nguyen, T., & Roberts, S. (2023). Professional identity factors in educator AI acceptance: A meta-analysis and conceptual framework. Teaching and Teacher Education, 123, 103956. https://doi.org/10.1016/j.tate.2022.103956

Ofosu-Ampong, N., Boateng, R., & Effah, J. (2023). Educators’ acceptance of artificial intelligence: An extension of the UTAUT model. Information Development, 39(2), 278–291. https://doi.org/10.1177/02666669221145633

Papakostas, M., Xanthopoulou, E., & Tsichla, E. (2024). Barriers to AI acceptance among educators: Insights from a European context. European Journal of Education Research, 13(1), 45–62. https://doi.org/10.12973/eu-jer.13.1.45

Patel, R., & Thompson, K. (2023). A meta-analysis of implementation approaches for educational AI: Comparing effectiveness across contexts. Educational Research Review, 39, 100504. https://doi.org/10.1016/j.edurev.2023.100504

Perrotta, C., & Selwyn, N. (2020). Deep learning and AI in education: Hype or hope? Learning, Media and Technology, 45(1), 1–5. https://doi.org/10.1080/17439884.2020.1686016

Peterson, R., & Zhang, Y. (2024). Meta-analysis of professional development approaches for AI implementation in education. Teaching and Teacher Education, 127, 104198. https://doi.org/10.1016/j.tate.2023.104198

Rahiman, H. U., & Kodikal, R. (2024). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1), Article 2293431. https://doi.org/10.1080/2331186X.2023.2293431

Roberts, T., & Chen, J. (2022). Emotional dimensions of AI acceptance among educators: A meta-analysis of affective factors. Learning and Instruction, 82, 101684. https://doi.org/10.1016/j.learninstruc.2022.101684

Rodriguez, S., & Kim, J. (2023). A meta-analysis of generative AI acceptance among educators: Comparing perceptions across tool types and educational contexts. Journal of Research on Technology in Education, 55(3), 312–336. https://doi.org/10.1080/15391523.2022.2136475

Sharma, P., & Johnson, L. (2023). Ethical dimensions of educator AI acceptance: A meta-analysis and framework. Journal of Educational Computing Research, 61(4), 1033–1062. https://doi.org/10.1177/07356331221167895

Stein, J. C., Banerjee, R., & Li, Y. (2024). AI-driven education and teacher training: A global overview. Teaching and Teacher Education, 131, 104179. https://doi.org/10.1016/j.tate.2023.104179

UNESCO. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000366994

UNESCO. (2023). AI and education: Guidance for policymakers. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000381539

U.S. Department of Education. (2024). Artificial intelligence and the future of teaching and learning: Insights and recommendations. Office of Educational Technology. https://tech.ed.gov/files/2024/01/AI-and-the-Future-of-Teaching-and-Learning.pdf

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

Walker, S., & Martinez, J. (2023). A meta-analysis of intervention effectiveness for improving educator AI acceptance. Educational Technology Research and Development, 71(3), 1287–1314. https://doi.org/10.1007/s11423-022-10154-4

Wang, X., & Johnson, P. (2023). A meta-analysis of educator acceptance factors for AI technologies in K–12 settings. Educational Research Review, 38, 100474. https://doi.org/10.1016/j.edurev.2023.100474

Wilson, M., & Johnson, K. (2022). Barriers to AI adoption in educational settings: A meta-analytic review across institutional types. Journal of Educational Computing Research, 60(3), 628–657. https://doi.org/10.1177/07356331221087654

Wong, L., & Davis, J. (2022). A meta-analysis of age and experience effects on educator AI acceptance. Computers & Education, 177, 104362. https://doi.org/10.1016/j.compedu.2021.104362

Downloads

Published

2025-09-30