Cited by 338Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEducation and Communication Studies

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Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness

Wang, Xinghua, Li, Linlin, Tan, Seng Chee, Yang, Lu, Lei, Jun·2023·Computers in Human Behavior

DOI 10.1016/j.chb.2023.107798

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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
Untitled work
(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
Systematic review of research on artificial intelligence applications in higher education – where are the educators?
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
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Locke, E. A. (1976) · Medical Entomology and Zoology · Cited by 6,819
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Parasuraman, A. (2000) · Journal of Service Research · doi:10.1177/109467050024001 · Cited by 3,328
Conceptualizing AI literacy: An exploratory review
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100041 · Cited by 2,205
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Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008) · Information Systems Research · doi:10.1287/isre.1070.0165 · Cited by 2,060
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Vrieze, S. I. (2012) · Psychological Methods · doi:10.1037/a0027127 · Cited by 1,948
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Kish-Gephart, J. J., Harrison, D. A., & Treviño, L. K. (2010) · Journal of Applied Psychology · doi:10.1037/a0017103 · Cited by 1,690
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(闫守轩), S. Y., & Yun, Y. (2020) · ECNU Review of Education · doi:10.1177/2096531120944929 · Cited by 105
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Brouillette, M. (2019) · Nature Medicine · doi:10.1038/s41591-019-0648-3 · Cited by 56
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van Leeuwen, A., & Rummel, N. (2020) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3375462.3375471 · Cited by 48
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Muggleton, S. H. 1., & Chater, N. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780198862536.001.0001 · Cited by 34
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Albacete, P., Jordan, P., Katz, S., Chounta, I., & McLaren, B. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23204-7_4 · Cited by 26
International Perspectives on the Role of Technology in Humanizing Higher Education
(2020) · Emerald Group Publishing Limited eBooks · doi:10.1108/s2055-3641202033 · Cited by 10
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Wang, X., & Wong, B. (2018) · Journal of Educational Computing Research · doi:10.1177/0735633118759455 · Cited by 7
Self-citation
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Walia, J. S., & Kumar, P. (2022) · International Journal of Virtual and Personal Learning Environments · doi:10.4018/ijvple.295310 · Cited by 6

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

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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
ORCID: Wang, Xinghua
Li, Linlin
Tan, Seng Chee
Yang, Lu
Lei, Jun
Venue: Computers in Human Behavior
Publisher: Elsevier BV
Published: 2023-09
Volume: 146
Pages: 107798
ISSN: 0747-5632 (ISSN-L)
DOI: 10.1016/j.chb.2023.107798
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
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

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