What Explains Teachers’ Trust in AI in Education Across Six Countries?
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 thei...
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| Vydané v: | International journal of artificial intelligence in education Ročník 35; číslo 3; s. 1288 - 1316 |
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| Hlavní autori: | , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer New York
01.09.2025
Springer Nature B.V |
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| ISSN: | 1560-4292, 1560-4306, 1560-4306 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Kizilcec, René F. Cukurova, Mutlu Kanemune, Susumu Tømte, Cathrine Viberg, Olga Alexandron, Giora Spikol, Daniel Coelho, Raquel Feldman-Maggor, Yael Wasson, Barbara Milrad, Marcelo Shirai, Shizuka |
| Author_xml | – sequence: 1 givenname: Olga orcidid: 0000-0002-8543-3774 surname: Viberg fullname: Viberg, Olga email: oviberg@kth.se organization: KTH Royal Institute of Technology, Digital Futures – sequence: 2 givenname: Mutlu orcidid: 0000-0001-5843-4854 surname: Cukurova fullname: Cukurova, Mutlu organization: University College London – sequence: 3 givenname: Yael orcidid: 0000-0002-0456-6664 surname: Feldman-Maggor fullname: Feldman-Maggor, Yael organization: KTH Royal Institute of Technology, Digital Futures, Weizmann Institute of Science – sequence: 4 givenname: Giora orcidid: 0000-0003-2676-6912 surname: Alexandron fullname: Alexandron, Giora organization: Weizmann Institute of Science – sequence: 5 givenname: Shizuka orcidid: 0000-0003-2676-3404 surname: Shirai fullname: Shirai, Shizuka organization: Osaka University – sequence: 6 givenname: Susumu surname: Kanemune fullname: Kanemune, Susumu organization: Osaka Electro-Communication University – sequence: 7 givenname: Barbara orcidid: 0000-0003-4897-1394 surname: Wasson fullname: Wasson, Barbara organization: University of Bergen, SLATE – sequence: 8 givenname: Cathrine orcidid: 0000-0003-0196-9772 surname: Tømte fullname: Tømte, Cathrine organization: University of Bergen, SLATE, University of Agder – sequence: 9 givenname: Daniel orcidid: 0000-0001-9454-0793 surname: Spikol fullname: Spikol, Daniel organization: University of Copenhagen – sequence: 10 givenname: Marcelo orcidid: 0000-0002-6937-345X surname: Milrad fullname: Milrad, Marcelo organization: Linnaeus University – sequence: 11 givenname: Raquel orcidid: 0000-0001-5073-2443 surname: Coelho fullname: Coelho, Raquel organization: University College London – sequence: 12 givenname: René F. orcidid: 0000-0001-6283-5546 surname: Kizilcec fullname: Kizilcec, René F. email: kizilcec@cornell.com organization: Cornell University |
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| Snippet | With growing expectations to use AI-based educational technology (AI-EdTech) to improve students’ learning outcomes and enrich teaching practice, teachers play... With growing expectations to use AI-based educational technology (AI-EdTech) to improve students' learning outcomes and enrich teaching practice, teachers play... |
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| SubjectTerms | Algorithms Artificial Intelligence Automation Beliefs Computer and Information Sciences Computer Science Computer Science Computers and Education Cultural Differences Cultural factors Culture Data- och informationsvetenskap Demographics Education Educational Technology Effectiveness Elementary Secondary Education Geographic Location Influence of Technology Interpersonal Communication Interpersonal Relationship Knowledge Level Learning Multiple regression analysis Outcomes of Education Psychological factors Self Efficacy Student Improvement Survey Teacher Characteristics Teacher Competencies Teachers Teaching Teaching Methods Technology adoption Trust Trust (Psychology) User Interfaces and Human Computer Interaction Values World Problems |
| Title | What Explains Teachers’ Trust in AI in Education Across Six Countries? |
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| Volume | 35 |
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