The DOI matches its Crossref record; 68 of the 69 references it cites were found in a registry or an index.
Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“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)
In the citing paper
“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
In the citing paper
“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
Citations
Cites
Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,603
Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921
Sources of method bias in social science research and recommendations on how to control it
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012) · Annual Review of Psychology · doi:10.1146/annurev-psych-120710-100452 · Cited by 15,744
Structural equation models with unobservable variables and measurement error: Algebra and statistics
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800313 · Cited by 13,287
(1998) · Cited by 12,725
\nThe use of partial least squares path modeling in international marketing
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009) · Radboud Repository (Radboud University) · doi:10.1108/s1474-7979(2009)0000020014 · Cited by 12,535
Handbook of Industrial and Organizational Psychology.
Hammer, T. H., Anderson, J. F., Gurdon, M. A., Jick, T. D., Petit, A., Stern, R. N., Schwartz, H., Vardi, Y., & Dunnette, M. D. (1977) · Administrative Science Quarterly · doi:10.2307/2391755 · Cited by 11,947
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242
The nature and causes of job satisfaction
Locke, E. A. (1976) · Medical Entomology and Zoology · Cited by 6,819
Research works printed but not linked: 1
- Langran, 2020, AI in teacher education. InSociety for information technology & teacher education international conference, Association for the Advancement of Computing in Education (AACE), 751
Cited by
Sorted by citations
In search of artificial intelligence (AI) literacy in teacher education: A scoping review
Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100169 · Cited by 498
Polyportis, A., & Pahos, N. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2317364 · Cited by 264
Fu, Y., & Weng, Z. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100306 · Cited by 195
Yang, L., & Zhao, S. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108337 · Cited by 164
Self-citationDerakhshan, A. (2025) · Learning and Motivation · doi:10.1016/j.lmot.2025.102114 · Cited by 151
Metadata
- 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
- Social Sciences › Education › 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
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
