Preparing for AI-enhanced education: conceptualizing and empirically examining teachers’ AI readiness
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Teachers with higher AIL used generative AI tools more effectively to generate ideas and solve problems, thereby supporting innovative behavior.
“For example, Wang et al. (2023) found that teachers with higher AIL used generative AI tools more effectively to generate ideas and solve problems, thereby supporting innovative behavior.”
From AI teaching innovation behavior among college teachers: a structural equation modeling analysis based on the AI-TPACK framework, teaching self-efficacy, professional identity, and AI literacy (2026) · cites this paper as Wang et al. (2023)
Higher AIL reduces perceived difficulty and risk in technology use, supporting innovation in authentic GenAI-supported teaching contexts.
“It also reduces perceived difficulty and risk in technology use, which supports innovation in authentic GenAI-supported teaching contexts (Wang et al., 2023).”
From AI teaching innovation behavior among college teachers: a structural equation modeling analysis based on the AI-TPACK framework, teaching self-efficacy, professional identity, and AI literacy (2026) · cites this paper as Wang et al., 2023
AIL development reduces perceived uncertainty and operational risk, supporting more sustained innovative practice.
“This development reduces perceived uncertainty and operational risk, thereby supporting more sustained innovative practice (Wang et al., 2023).”
From AI teaching innovation behavior among college teachers: a structural equation modeling analysis based on the AI-TPACK framework, teaching self-efficacy, professional identity, and AI literacy (2026) · cites this paper as Wang et al., 2023
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- Authors
- Wang, X., Li, L., Tan, S. C., Yang, L., & Lei, J.
- Affiliations
- Qingdao University (CN)
- Nanyang Technological University (SG)
- Ningbo University (CN)
- Corresponding author
- Wang, Xinghua
- Lei, Jun
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 146
- Pages
- 107798
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W4367189069
- Semantic Scholar
- 7b9860fde4f90b3b5ac2128be447d0299472e1c2
- Cited by
- 336
- References
- 69
- Citations by year
- 2026: 128 · 2025: 122 · 2024: 76 · 2023: 10
- FWCI
- 78.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers in Human Behavior (publishedVersion)
- NIE Digital Repository (National Institute of Education, Nanyang Technological University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Education and Communication Studies
- Keywords
- AI readiness, teachers, artificial intelligence in education, teacher job satisfaction, AI ethics, primary school teachers
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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.
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