What Explains Teachers' Trust of AI in Education across Six Countries?
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2023.
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Citing papers per year: 2025: 3; 2026: 4.
7 papers.
- 2025Measuring and identifying factors of individuals' trust in Large Language Modelscited by 7
- 2025Artificial Intelligence in Pronunciation Teaching: Use and Beliefs of Foreign Language Teacherscited by 0
- 2025Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practicescited by 0
- 2026Understanding Teacher Revisions of Large Language Model-Generated Feedbackcited by 0
- 2026Teachers' Perceived Benefits and Risks of AI Across Fifty-Five Countries: An Audit of LLM Alignment and Steerabilitycited by 0
- 2026Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instructioncited by 0
- 2026Addressing Trust in AI Systems through Education: A Didactic Perspectivecited by 0
- 2026Addressing Trust in AI Systems through Education: A Didactic Perspective2 Sep 2026
- 2026Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction16 Jun 2026
- 2026Teachers' Perceived Benefits and Risks of AI Across Fifty-Five Countries: An Audit of LLM Alignment and Steerability8 May 2026
- 2026Understanding Teacher Revisions of Large Language Model-Generated Feedback29 Mar 2026
- 2025Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices13 Sep 2025
- 2025Artificial Intelligence in Pronunciation Teaching: Use and Beliefs of Foreign Language Teachers6 Mar 2025
- 2025Measuring and identifying factors of individuals' trust in Large Language Models28 Feb 2025