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What Explains Teachers’ Trust in AI in Education Across Six Countries?
Open accessAbstract
With growing expectations to use AI-based educational technology (AI-EdTech) to improve students’ learning outcomes and enrich teaching practice, teachers play a central role in the adoption of AI-EdTech in classrooms. Teachers’ willingness to accept vulnerability by integrating technology into their everyday teaching practice, that is, their trust in AI-EdTech, will depend on how much they expect it to benefit them versus how many concerns it raises for them. In this study, we surveyed 508 K-12 teachers across six countries on four continents to understand which teacher characteristics shape teachers’ trust in AI-EdTech, and its proposed antecedents, perceived benefits and concerns about AI-EdTech. We examined a comprehensive set of characteristics including demographic and professional characteristics (age, gender, subject, years of experience, etc.), cultural values (Hofstede’s cultural dimensions), geographic locations (Brazil, Israel, Japan, Norway, Sweden, USA), and psychological factors (self-efficacy and understanding). Using multiple regression analysis, we found that teachers with higher AI-EdTech self-efficacy and AI understanding perceive more benefits, fewer concerns, and report more trust in AI-EdTech. We also found geographic and cultural differences in teachers’ trust in AI-EdTech, but no demographic differences emerged based on their age, gender, or level of education. The findings provide a comprehensive, international account of factors associated with teachers’ trust in AI-EdTech. Efforts to raise teachers’ understanding of, and trust in AI-EdTech, while considering their cultural values are encouraged to support its adoption in K-12 education.
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“Existing research on teachers' trust in GenAI-based educational tools largely centers on K-12 education [13, 82, 83, 94, 114].”
From Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education (2025) · cites this paper as [114]
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Citations
Cites
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
An integrative model of organizational trust
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995) · The Academy of Management Review · doi:10.2307/258792 · Cited by 10,736
Self-efficacy: Toward a unifying theory of behavioral change
Bandura, A. (1978) · Advances in Behaviour Research and Therapy · doi:10.1016/0146-6402(78)90002-4 · Cited by 10,586
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · cited 1×
“Over the last decade, artificial intelligence-based educational technology (AI-EdTech) has been increasingly used in K-12 (kindergarten, primary and secondary) education across many countries (Holmes et al., 2022; UNESCO, 2023; Zawacki-Richter et al., 2019).”
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 1×
“Given the inherent challenges in uncertainty avoidance with these technologies (Bender et al., 2021), their integration into AI-EdTech requires careful investigation.”
(2010) · Cited by 3,924
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232 · cited 1×
“Concerns about AI have also been framed more broadly as algorithm aversion in AI-adoption research in other domains (Dietvorst et al., 2015).”
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
TRUST: THE CONNECTING LINK BETWEEN ORGANIZATIONAL THEORY AND PHILOSOPHICAL ETHICS
Hosmer, L. T. (1995) · The Academy of Management Review · doi:10.5465/amr.1995.9507312923 · Cited by 2,323
Flagged first, then by citations
Research works printed but not linked: 2
- Slovic, 1993, Perceived risk, trust, and democracy, Risk Analysis, 13, 675, 10.1111/j.1539-6924.1993.tb01329.x · DOI
- Wang, 2021, Factors affecting the adoption of ai-based applications in higher education, Educational Technology & Society, 24, 116
Cited by
Sorted by citations
Items 101–179 of 179
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Din, A. M. G. E. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4908-4.ch001
Tapia-Sobarzo, J., Peters, B., Puraivan, E., Rodríguez, M., Gamboa, M., & Hervás-Gómez, C. (2025) · International Conference of the Chilean Computer Science Society · doi:10.1109/sccc67219.2025.11420529
Schwanz, R., Fox, E. L., Lui, T., Alarcon, G. M., & Capiola, A. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2638544
Kaba, A., Shimray, S. R., Zaitoun, E. A., & Refae, G. A. E. (2026) · Open Information Science · doi:10.1515/opis-2025-0038
Learning in the Cloud, Drifting in the Mind
Din, A. M. G. E. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4222-1.ch009
He, M., Huang, Z., Sima, X., & Zhu, K. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-8420-8_30
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Kaur, G., & Shah, M. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-3510-9.ch008
Garba, S. A. (2026) · Springer international handbooks of education · doi:10.1007/978-981-95-2423-5_7
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Mehić, T., Jacobs, M.-B. A., Kowalke, V., Salinas, L. D. R., Quentin, J., Müller, F. K., & Baumgart, N. (2026) · Information - Wissenschaft & Praxis · doi:10.1515/iwp-2026-2005
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Xu, X. (2026) · International Conference on Informatics Education and Computer Technology Applications (IECA) · doi:10.1145/3802133.3802149
Amiscosa, A. N. (2026) · European Modern Studies Journal · doi:10.59573/emsj.10(1).2026.52
Yang, Z., Chen, G. P., Chotia, V., Malik, P., Quacquarelli, B., & Cillo, V. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-03-2026-0471
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Anthis, Z., & Kyriakidou-Zacharoudiou, A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100610 · Cited by 1
Jiang, Y., Wang, Z., Wang, Y., Zhang, Y., Xu, Y., & Wu, C. (2026) · International Conference on Educational and Information Technology · doi:10.1109/iceit68991.2026.11521608
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Das, S. D., & Bala, P. K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0467-8.ch010
Yang, F.-C. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14040-x
Oh, N., Jiang, Q., Ko, E., Taylor, C. R., & Kim, K. H. (2026) · Journal of Global Fashion Marketing · doi:10.1080/20932685.2026.2682791
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Abniki, R., Imanifard, A., & Majidi, B. (2026) · International Biennial Conference on Advances in Artificial Intelligence and Data Science (IBAAIDS) · doi:10.1109/ibaaids66771.2026.11567407
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Ch, N. A. N., Wong, A. Y., Corcoran, B., Barbot, H., Ahmad, Y., & Mills, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29763-1_27
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Borchers, C., Rodrigues, L., da Costa, N. T., Xavier, C., & Mello, R. F. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29763-1_5 · Cited by 1
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Almasri, M. J., Imran, A. S., Pireva, K., Mavroudi, A., & Soylu, A. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574185
Pratama, Y., Sariyatun, S., & Ardiyanto, D. T. (2026) · Research Square · doi:10.21203/rs.3.rs-10266491/v1
Vasiliou, C., & Bonareva, V. (2026) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3805029.3818293
Alubthane, F. O. (2026) · Education Sciences · doi:10.3390/educsci16071118
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Lyublinskaya, I., & Du, X. (2026) · Springer texts in education · doi:10.1007/978-3-032-23570-1_1
Lyublinskaya, I., & Du, X. (2026) · Springer texts in education · doi:10.1007/978-3-032-23570-1_2
Trust by design: Human factors and trust calibration in ML-based educational systems
Anthis, Z., & Kyriakidou-Zacharoudiou, A. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103893
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Ghosh, P., Shetty, A., G, B. B., Shashi Kumar, M., & Muneshwara, M. S. (2025) · IEEE International Conference on Innovate for Humanitarian: Tech Solutions for Global Challenge (ICIH) · doi:10.1109/icih67754.2025.11608817
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Fu, Y., & Hanaki, N. (2026) · Journal of Economic Interaction and Coordination · doi:10.1007/s11403-026-00490-6 · Cited by 1
Luo, Y., Fan, Y., Wang, T., Xu, Q., & Zhao, W. (2026) · Behavioral Sciences · doi:10.3390/bs16081313
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Qiao, S., Nie, Y., Niu, F., He, Z., & Chen, L. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1902647 · Cited by 1
Zhu, J., Chen, J., Covarrubias, M., Li, D., & Wei, W. (2026) · Sustainability · doi:10.3390/su18157986 · Cited by 1
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Wu, J., Liu, B., & Zhao, Q. (2026) · International Journal of Educational Research · doi:10.1016/j.ijer.2026.103088
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Bi, T., Dalelovna, S. A., Lyu, A., & Xiu, S. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1890508 · Cited by 1
Rodrigues, E. G. F., & Challco, G. C. (2026) · Revista Tecnologias Educacionais em Rede (ReTER) · doi:10.5902/2675995097179
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Mesenhöller, J., & Böhme, K. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1875856
Zhang, X., & Reiss, M. J. (2026) · International Journal of Science Education · doi:10.1080/09500693.2026.2733687
Ho, T. M., Luong, M.-V., Tran, T.-K., Phan, T.-H., Nguyen, H. T., Ghotbi, N., Pham, H., & Ho, M. (2026) · Societal Impacts · doi:10.1016/j.socimp.2026.100209
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Marjerison, R. K., Jeun, H. K., Schwirzke, K., & Kim, J. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14159-x
Xiu, R. L., Aguilar, S. J., Macias, A. J., Shikanova, A., Al-Sulaiti, R., Junjunia, M., & Jebril, S. T. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1929017 · Cited by 2
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Çelik, İ., Laru, J., Zabolotna, K., Jokela, I., & Mäkitalo, K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37979-5_9
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Eren, C. D., & Geçer, A. K. (2026) · Acta Infologica · doi:10.26650/acin.1893001
Karabatak, S., Alanoğlu, M., Polat, S., Yurtçu, M., DEMİR, Y., Ayaz, E., Kuloğlu, A., Santur, Y., Aydoğan, M., İncidelen, M., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-11031270/v1
Wang, X., & Xiaoyun, W. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1973587
Wang, J., & Sim, Y. (2026) · Behavioral Sciences · doi:10.3390/bs16101930
Mohamed, R. K. M. H., Annamalai, N., Ganesan, Y., & Hermanto, . (2026) · Contemporary Educational Technology · doi:10.30935/cedtech/19637
Sánchez, D. T. P., & Uribe-Arengas, J. J. (2026) · Discover Education · doi:10.1007/s44217-026-02316-w
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An, N. T. K. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.103943
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Metadata
- Authors
- Viberg, O., Cukurova, M., Feldman-Maggor, Y., Alexandron, G., Shirai, S., Kanemune, S., Wasson, B., Tømte, C., Spikol, D., Milrad, M., Coelho, R., & Kizilcec, R. F.
- Affiliations
- Digital Futures (SE)
- University College London (GB)
- Weizmann Institute of Science (IL)
- The University of Osaka (JP)
- Osaka Electro-Communication University (JP)
- University of Bergen (NO)
- University of Agder (NO)
- University of Copenhagen (DK)
- Linnaeus University (SE)
- Cornell University (US)
- Corresponding author
- Viberg, Olga
- Kizilcec, René F.
- ORCID
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2025-09
- Volume
- 35
- Issue
- 3
- Pages
- 1288-1316
- ISSN
- 1560-4292 (ISSN-L)
- OpenAlex
- W4403422104
- Cited by
- 179
- References
- 62
- Citations by year
- 2026: 115 · 2025: 60
- FWCI
- 67.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Artificial Intelligence in Education (publishedVersion, cc-by)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Diva portal (Dalarna University Library) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Funders
- Trond Mohn Research Foundation, Norway (Artificial Intelligence in Education: Layers of Trust (EduTrust AI))
- National Science Foundation, US (award no. 2237593)
- Royal Institute of Technology
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Education and Technology Integration
- Keywords
- K-12 teachers, technology acceptance, self-efficacy, AI understanding, cultural values, Hofstede cultural dimensions, cross-national comparison, teacher characteristics, technology adoption in classrooms
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