Developing and validating an instrument for teachers’ acceptance of artificial intelligence in education
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Pre-service teachers emphasized the importance of perceived usefulness and ease of use in shaping their adoption of AI tools.
“They also emphasized the importance of perceived usefulness and ease of use in shaping their adoption of AI tools, aligning with literature findings (Guo et al., 2025).”
From Integrating artificial intelligence into preservice teacher education: enhancing AI literacy and digital material design competency (2026) · cites this paper as Guo et al., 2025
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References 67 · Cited by 35
- Authors
- Guo, S., Shi, L., & Zhai, X.
- Affiliations
- Nanjing Normal University (CN)
- University of Georgia (US)
- Venue
- Education and Information Technologies
- Published
- 2025-01-22
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4406707699
- Cited by
- 35
- References
- 67
- Citations by year
- 2026: 26 · 2025: 9
- FWCI
- 36.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Education and Information Technologies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- artificial intelligence in education, technology acceptance, instrument development, scale validation, pre-service teachers, perceived usefulness, perceived ease of use, behavioral intention, self-efficacy, anxiety, think-aloud
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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