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.
“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]
The Conclusions (p. 1311) identify this as a study of teachers’ trust in AI-EdTech in K–12 education, and the ‘Sources of Variation in Teachers’ Trust in AI-EdTech’ section discusses prior K–12 studies. But the paper does not establish that existing research largely centers on K–12 or specifically concerns GenAI-based tools; it proposes examining trust by application type, including generative AI, in future work (p. 1310). (LLM, conf=0.83)
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- 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 · 2024: 4
- 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
- Physical Sciences › Computer Science › Information Systems › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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