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Cited by 302Artificial Intelligence in EducationDigital literacy in educationTeacher Education and Leadership Studies

Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis

Open access
Zhao, Leilei, Wu, Xiaofan, Luo, Heng·2022·Sustainability
DOI 10.3390/su142114549
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Abstract

As smart technology promotes the development of various industries, artificial intelligence (AI) has also become an important driving force for innovation and transformation in education. For teachers, how to skillfully apply AI in teaching and improve their AI literacy has become a necessary goal for their sustainable professional development. This research examines the correlations among the dimensions of AI literacy of teachers in order to promote the effectiveness of class teaching and the adoption of artificial intelligence literacy (AIL). Our findings are based on the analysis of 1013 survey results, where we tested the level of AI literacy of teachers, including Knowing and Understanding AI (KUAI), Applying AI (AAI), Evaluating AI Application (EAIA), and AI Ethics (AIE). We find that AAI had a significant, positive effect on the other three dimensions. Thus, based on the analysis, the government should take action to cultivate teachers’ AI literacy. In order to improve teachers’ AI literacy, the choice of curriculum, content, methods, and practical resources for special training should be diverse and committed to making AI literacy an essential enabler for teachers’ sustainable future development.

AI snapshot

Written from the paper’s full text.

TL;DR

This study surveyed 1013 primary and middle school teachers in Jiangsu, China, to model relationships among four dimensions of AI literacy: Knowing and Understanding AI, Applying AI, Evaluating AI Application, and AI Ethics. Applying AI was the strongest positive predictor of the other dimensions, especially Knowing and Understanding AI.

Key findings

  • Applying AI had a significant positive effect on Knowing and Understanding AI (β = 0.88, p < 0.001), Evaluating AI Application (β = 0.11, p < 0.001), and AI Ethics (β = 0.53, p < 0.001).
  • Knowing and Understanding AI positively predicted Evaluating AI Application (β = 0.82, p < 0.001), and AI Ethics positively predicted Knowing and Understanding AI (β = 0.17, p < 0.001).
  • The hypothesized path from AI Ethics to Evaluating AI Application (H3b) was not supported; the paper reports H3 as partially supported.
  • Mean AI literacy ratings were above the neutral point: KUAI 3.636, AAI 3.457, EAIA 3.449, and AIE 4.007 on a 5-point scale; the structural model showed acceptable fit (CFI = 0.911, RMSEA = 0.079, SRMR = 0.060).

Methods

  • A one-month 2022 survey of teachers in Jiangsu, China, working in formal schools from grade one to grade twelve with at least one year of teaching experience.
  • From 1271 retrieved questionnaires, 56 invalid responses were removed, rural submissions were randomly trimmed, and 1013 samples were analyzed; participants were 57.5% primary (n = 581), 42.2% secondary (n = 429), and 0.3% higher education (n = 3).
  • AI literacy was measured with 20 items across four dimensions using a 5-point Likert scale; reliability was high (Cronbach’s α = 0.963, McDonald’s ω = 0.967), and structural equation modeling was conducted in AMOS.

Limitations

  • The paper states that the sample was not randomly selected and most teachers were from rural areas in China, so results should not be generalized to all Chinese teachers.
  • The paper states that data were one-time and quantitative; future research should add qualitative and long-term data.
  • Evident from the text: the study relies on self-reported Likert-scale responses and a cross-sectional design, so causal direction among the AI literacy dimensions is not established.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/su142114549

Cites

67 of 67 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 27 cited sources checked · 0 supported · 0 flagged

  • Findingown data
    § Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis · p. 1

    Applying AI (AAI) had a significant, positive effect on the other three AI literacy dimensions: Knowing and Understanding AI (KUAI), Evaluating AI Application (EAIA), and AI Ethics (AIE).

    “Wefind that AAI had a significant, positive effect on the other three dimensions.”
    Quote in the paperSubstantial claim
  • Findingown data
    § 4.4. Hypotheses Testing Results · p. 9

    AI Ethics has a positive effect on Knowing and Understanding AI (β = 0.17, p < 0.001) among the study's sample.

    “AI Ethics positively affect Knowing and Understanding AI ( β = 0.17, p &lt; 0.001).”
    • Also stated in: § 5.1. Varying Impact on Knowing and Understanding AI · p. 10 · plain
      “The fact that AI Ethics has only a moderate effect on Knowing and Understanding AI is not acceptable ( β = 0.17)”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The analyzed model assumes correlations among dimensions of AI literacy.
  • Findingown data
    § 4.4. Hypotheses Testing Results · p. 9

    Applying AI has a positive effect on AI Ethics (β = 0.53, p < 0.001) among the study's sample.

    “Appling AI had the least significant positive effects on evaluating and creating AI ( β = 0.11, p &lt; 0.001) and was lower than that of AI Ethics ( β = 0.53, p &lt; 0.001).”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The analyzed model assumes correlations among dimensions of AI literacy.
  • Findingown data
    § 4.4. Hypotheses Testing Results · p. 9

    Applying AI has a positive effect on Knowing and Understanding AI (β = 0.88, p < 0.001) among the study's sample.

    “Moreover, Applying AI had the most positive effect on Knowing and Understanding AI ( β = 0.88, p &lt; 0.001).”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The analyzed model assumes correlations among dimensions of AI literacy.
  • Findingnone given
    § 6. Conclusions and Implications · p. 11

    As the level of education increases, the level of AI literacy decreases.

    “Resonably, the results obtained show that, as the level of education increases, the level decreases.”
    Quote in the paperSubstantial claim
  • May combine two claims. 16
  • Classified as probably not a substantial claim. 17
  • The quote only partly states this claim. 1

  • May combine two claims.

    Findingown data
    § 5. Discussion · p. 9

    The majority of teachers in the study are at a moderate to high level of AI literacy and have a relatively in-depth understanding of the use of AI resources in the classroom.

    “this study shows that the majority of teachers are at a moderate to high level of AI literacy and have a relatively in-depth understanding of the use of AI resources in the classroom.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § 4.1. Descriptive and Correlational Statistics · p. 7

    Mean ratings for KUAI, AAI, EAIA, and AIE were all above the neutral point of 3 on the five-point Likert scale, ranging from 3.45 to 4.01.

    “The mean ratings for Knowing and Understanding AI (KUAI), Applying AI (AAI), Evaluating AI Application (EAIA), and AI Ethics (AIE) were all above the neutral point (i.e., 3 on the five-point Likert scale), ranging from 3.45 to 4.01”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Findingnone given
    § 5. Discussion · p. 10

    Most teachers in the study have not received AI education.

    “most teachers in this study have not received AI education”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § 4.3. Structural Model · p. 8

    The structural model fit the empirical data well: CFI = 0.911, RMSEA = 0.079, SRMR < 0.08, and the Chi-square test p < 0.001.

    “All the indices indicated good fit: CFI = 0.911, RMSEA = 0.079, and the Chi-square test ( p &lt; 0.001), as is shown in Table 4. We also adopted the standardized root mean square residual (SRMR) to evaluate the model fit cautiously, and the value of SRMR was lower than 0.08, which was acceptable, according to Hair et al. (2017) [61].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [61] Hair, 2017, PLS-SEM or CB-SEM: Updated guidelines on which method to use, Int. J. Multivar. Data Anal. source

      Not checked in the free run.

  • May combine two claims.

    Findingreasoning
    § 5. Discussion · p. 10

    Educational background is closely related to AI literacy, with better educational background associated with higher AI literacy related to teachers' performance at class.

    “educational background is closely related to AI literacy (where the better the educational background, the higher is the degree of AI literacy related to teachers' performance at class).”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Findingown data
    § 4.4. Hypotheses Testing Results · p. 9

    Knowing and Understanding AI has a positive effect on Evaluating AI Application (β = 0.82, p < 0.001) among the study's sample of Chinese primary and middle school teachers.

    “Hypothesis 1 is supported. Knowing and Understanding AI was a significant antecedent to evaluating and creating AI ( β = 0.82, p &lt; 0.001).”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The analyzed model assumes correlations among dimensions of AI literacy.

  • May combine two claims.

    Study design and conductcitation
    § 3.2. Participants · p. 6

    Gender did not lead to bias in this survey because women are the majority of practitioners in the social sciences, especially in education.

    “Considering the predominance of female participants in this study, gender did not lead to bias in this survey, as women are the majority of practitioners in the social sciences, especially in education [56].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [56] Gialamas, 2013, Student teachers’ perceptions about the impact of internet usage on their learning and jobs, Comput. Educ. source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data
    § 3.3. Instrument · p. 6

    The final scale consisted of 20 items divided into four dimensions, and the correlation of items across dimensions was lower than 0.4.

    “The final scale consisted of 20 items divided into four dimensions, among different dimensions, and the correlation of items was lower than 0.4.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2.1.4. Evaluating AI Application · p. 3

    Evaluating AI applications could be beneficial for enhancing teachers' motivation and interest in teaching with AI.

    “Evaluating AI applications could be beneficial for enhancing their motivation and interest in teaching with AI [40].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [40] Rapanta, 2020, Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity, Postdigital Sci. Educ. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2.2.2. Relationships among Applying AI with Other Aspects · p. 4

    Teachers paid no attention to ethical concerns such as biased decisions, lack of transparency, privacy issues, and the risk of invasion of privacy.

    “which found that teachers paid no attention to ethical concerns, such as biased decisions, lack of transparency, privacy issues, and the risk of invasion of privacy [52,53].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [52] Lee, H.S., and Lee, J. (2021). Applying Artificial Intelligence in physical education and future perspectives. Sustainability, 13. source

      Not checked in the free run.

    • cites [53] Shah, 2021, An iSchool approach to data science: Human-centered, socially responsible, and context-driven, J. Assoc. Inf. Sci. Technol. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation
    § 3.3. Instrument · p. 7

    A McDonald's omega coefficient greater than 0.9 indicates high quality of reliability of the study data.

    “As Ravinder suggested, when the McDonald's omega coefficient is greater than 0.9, it indicates a high quality of reliability of the study data [57].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [57] Ravinder, 2020, Literature Review of Cronbach alpha coefficient (A) And Mcdonald’s Omega Coefficient (Ω), Eur. J. Mol. Clin. Med.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given
    § 3.2. Participants · p. 5

    A systematic random sampling of teachers was conducted, controlling for the number in the sample within different subjects.

    “A systematic random sampling of teachers was conducted, controlling for the number in the sample within different subjects.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data
    § 3.3. Instrument · p. 7

    Cronbach's alpha and McDonald's omega coefficients for the scale were 0.963 and 0.967, respectively.

    “The Cronbach's alpha and McDonald's omega coefficients for this scale were tested to be 0.963 and 0.967, respectively.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductown data
    § 3.3. Instrument · p. 6

    The KMO test was 0.968 and the Bartlett test was significant (χ2 = 28,200.756, p < 0.05).

    “The KMO test (Kaiser-Meyer-Olkin) was 0.968 and the Bartlett test was significant ( χ 2 = 28,200.756, p &lt; 0.05).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation
    § 2.1.4. Evaluating AI Application · p. 3

    Evaluating AI applications is an important competency that enables teachers to judiciously evaluate AI technologies and comment on the application of AI in teaching.

    “Apart from the ethics of understanding and using AI, evaluating artificial intelligence applications is an important competency that enables teachers to judiciously evaluate AI technologies, commenting on the application of AI in teaching [41].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [41] Huang, 2021, Aims for cultivating students’ key competencies based on Artificial Intelligence education in China, Educ. Inf. Technol. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Study design and conductnone given
    § 3.2. Participants · p. 5

    The study was conducted following ethical research codes and all respondents participated in the survey voluntarily.

    “This study was conducted following ethical research codes and all respondents participated in the survey voluntarily.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisecitation
    § 1. Introduction · p. 2

    AI literacy can be divided into four common dimensions: understanding fundamental AI concepts, applying AI concepts in practical contexts, critically evaluating and engaging with AI technologies, and understanding the ethical implications of AI applications.

    “According to the study of Ng et al., AI literacy can be divided into four common dimensions to build structure”
    • Also stated in: § 2.1.1. AI Literacy · p. 2 · plain
      “While AI literacy is not equal to the applications of AI, following the conclusion of Bloom, it includes four dimensions: Knowing and Understanding AI, Applying AI, Evaluating AI Application and AI Ethics, as identified from previous studies [23-25].”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [9] Ng, 2021, AI literacy: Definition, teaching, evaluation and ethical issues, Proc. Assoc. Inf. Sci. Technol. source

      Not checked in the free run.

    • cites [23] Cope, 2021, Artificial Intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies, Educ. Philos. Theory source

      Not checked in the free run.

    • cites [24] Acemoglu, D., and Restrepo, P. (2018). Artificial Intelligence, automation, and work. The Economics of Artificial Intelligence: An Agenda, University of Chicago Press. source

      Not checked in the free run.

    • cites [25] Kong, 2021, Evaluation of an Artificial Intelligence literacy course for university students with diverse study backgrounds, Comput. Educ. Artif. Intell. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2.1.2. Knowing and Understanding AI · p. 2

    A poor belief state in the potential of AI among teachers has been proven.

    “Meanwhile, a poor belief state in the potential of AI among teachers has also been proven [28,29].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [28] Vazhayil, A., Shetty, R., Bhavani, R.R., and Akshay, N. (2019, January 9–11). Focusing on teacher education to introduce AI in schools: Perspectives and illustrative findings. Proceedings of the 2019 IEEE Tenth International Conference on Technology for Education (T4E), Goa, India. source

      Not checked in the free run.

    • cites [29] Edwards, 2018, I, teacher: Using Artificial Intelligence (AI) and social robots in communication and instruction, Commun. Educ. source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation
    § 4.2. Measurement Model · p. 8

    A Cronbach's alpha value above 0.7 indicates high reliability of measurement.

    “According to Nunnally (1978), the α value above 0.7 indicates a high reliability of measurement.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [60] Nunnally, 1978, Psychometric theory, Am. J. Educ. Res.

      Not checked in the free run.

  • May combine two claims.

    Study design and conductnone given
    § 7. Limitations and Future Research · p. 12

    The sample from this study was not randomly selected and the majority of teachers were from rural areas in China.

    “The sample from this study was not randomly selected and the majority of teachers were from rural areas in China.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning
    § 5. Discussion · p. 10

    Teachers' previous technological skills help them to know and understand AI better.

    “thus their previous technological skills help them to know and understand AI better.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning
    § 5.1. Varying Impact on Knowing and Understanding AI · p. 10

    Except for AI Ethics, there are other cultural or technical reasons behind the relationship between AI Ethics and Knowing and Understanding AI.

    “which means except AI Ethics, there are other cultural or technical reasons behind the relationship.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Many Chinese teachers have insufficient resources to develop the AI literacy of Knowing and Understanding AI.
    • The authors state this as an assumption: The analyzed model assumes correlations among dimensions of AI literacy.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § 5.1. Varying Impact on Knowing and Understanding AI · p. 10

    Knowing and Understanding AI is influenced by multiple factors, and AI Ethics can be seen as a factor contributing to its enhancement.

    “Such a discovery is consistent with Cetindamar's study [66], Knowing and Understanding AI is influenced by multiple factors, and AI Ethics can be seen as a factor contributing to its enhancement.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [66] Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., and Knight, S. (2022). Explicating AI literacy of employees at digital workplaces. IEEE Trans. Eng. Manag., 1–13.

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given
    § 7. Limitations and Future Research · p. 12

    The data collected were one-time and quantitative in nature.

    “The other limitation of the study lies in the fact that the data collected were one-time and quantitative in nature.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Premisecitation
    § 6. Conclusions and Implications · p. 11

    The lack of teachers' AI literacy can be attributed to the over-intellectualization of primary education and the focus on achievement at the expense of the learning and teaching process, resulting in the neglect of teachers' AI literacy enhancement.

    “the lack of teachers' AI literacy can be attributed to the over-intellectualization of primary education and the focus on achievement at the expense of the learning and teaching process, resulting in the neglect of teachers' AI literacy enhancement [76,77].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [76] Dilmurod, 2021, Prospects for the introduction of artificial intelligence technologies in higher education, ACADEMICIA: an international multidisciplinary research journal.

      Not checked in the free run.

    • cites [77] Papakonstantinou, A., Batsaris, M., Spondylidis, S., and Topouzelis, K. (2021). A citizen science unmanned aerial system data acquisition protocol and deep learning techniques for the automatic detection and mapping of marine litter concentrations in the coastal zone. Drones, 5. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given
    § 7. Limitations and Future Research · p. 12

    The study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of Jiangnan University (protocol code JNU20220901IRB12, date of approval 16 September 2022).

    “ThestudywasconductedinaccordancewiththeDeclaration of Helsinki, and approved by the Medical Ethics Committee of Jiangnan University (protocol code JNU20220901IRB12 and date of approval 16 September 2022).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given
    § 7. Limitations and Future Research · p. 12

    Informed consent was obtained from all subjects involved in the study.

    “Informedconsentwasobtainedfromallsubjectsinvolvedinthestudy.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data
    § 4.2. Measurement Model · p. 8

    All Cronbach's alpha values were greater than 0.7, suggesting acceptable reliability for the questionnaire and its sub-sections.

    “In this study, all α values were greater than 0.7, suggesting an acceptable reliability for the questionnaire and its sub-sections [60].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [60] Nunnally, 1978, Psychometric theory, Am. J. Educ. Res.

      Not checked in the free run.

28 of 28

  • The paper relies on this without support.

    A large number of teachers have been exposed to AI-enabled appliances but do not understand fundamental AI concepts such as big data structures, computational thinking concepts, or ubiquitous computing approaches.

    “A large number of teachers have been exposed to the technological environment with AI-enabled appliances, but in reality, they do not understand the fundamental concepts, such as big data structures, computational thinking concepts, or ubiquitous computing approaches.”

  • The paper relies on this without support.

    The pyramid-shaped distribution of participants mirrors the teacher composition structure in China.

    “Such pyramid-shaped distribution mirrors the teacher composition structure in China.”

  • The paper relies on this without support.

    AI literacy in teacher education is in its infancy.

    “Artificial Intelligence literacy in teacher education is in its infancy.”

  • The paper relies on this without support.

    Many Chinese teachers have insufficient resources to develop the AI literacy of Knowing and Understanding AI.

    “In fact, many Chinese teachers have insufficient resources to develop the AI literacy of Knowing and Understanding AI.”

  • The authors state this as an assumption.

    The analyzed model assumes correlations among dimensions of AI literacy.

    “the analyzed model assumes the correlations among dimensions of AI literacy”
5 of 5

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Sampling is described as both random and nonrandom

    Major

    The methods state that systematic random sampling was conducted, whereas the limitations state that the study sample was not randomly selected.

  • Educational-stage percentages differ from Table 1

    Minor

    The prose reports 57.5% primary and 42.2% secondary teachers, while Table 1 gives 57.4% and 42.3% for the same counts.

  • Significant chi-square is counted as evidence of good fit

    Minor

    The methods identify a non-significant chi-square as indicating good fit, but the results include a chi-square test with p < 0.001 among indices said to indicate good fit.

  • Three participants are assigned conflicting educational stages

    Minor

    The prose says three participants taught in higher education, but the study describes participants as teaching grades one through twelve and Table 1 classifies the three as high-school teachers.

4 of 4

Cites

Flagged first, then by citations

Research works printed but not linked: 8

  1. McBride, C. (2015). Children’s Literacy Development: A Cross-Cultural Perspective on Learning to Read and Write, Routledge.
  2. Zhang, 2021, Artificial Intelligence Empowers Education Evaluation: The New Concept and Core Elements of “Integration of Academic Evaluation”, China Distance Educ., 2, 1
  3. 2016, Students of Teaching Degrees: ICTs and disabilities, Rev. Electrónica Investig. Educ., 18, 106
  4. Hu, 2021, Construction of Intelligent Education Literacy Framework for K-12 Teachers, Open Educ. Res., 4, 59
  5. Wang, 2021, Factors Affecting the Adoption of AI-Based Applications in Higher Education, Educ. Technol. Soc., 24, 116
  6. Guo, 2021, Teachers’ role orientation and literacy framework in the age of intelligence, China Electron. Educ., 6, 121
  7. Liu, 2020, Exploration on the intelligent education literacy of teachers in the era of Artificial Intelligence, Mod. Educ. Technol., 11, 12
  8. Ravinder, 2020, Literature Review of Cronbach alpha coefficient (A) And Mcdonald’s Omega Coefficient (Ω), Eur. J. Mol. Clin. Med., 7, 2943

Cited by

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Related

Authors
Zhao, L., Wu, X., & Luo, H.
Affiliations
  • Jiangnan University (CN)
  • Central China Normal University (CN)
Corresponding author
  • Wu, Xiaofan
  • Luo, Heng
ORCID
Venue
Sustainability
Publisher
MDPI AG
Published
2022-11-05
Volume
14
Issue
21
Pages
14549
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su142114549
OpenAlex
W4308453112
Semantic Scholar
37c6c922a4f85797f1c7462faaab43819c52c639
Cited by
239
References
66
Citations by year
2026: 83 · 2025: 99 · 2024: 51 · 2023: 6
FWCI
38.60
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
China Children’s Center Children’s Artificial Intelligence Education Institute (CNCCYJY202202)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Information Systems › Digital literacy in education
  • Social Sciences › Education › Teacher Education and Leadership Studies
Keywords
AI literacy, teacher professional development, primary school teachers, junior high school teachers, AI ethics, teacher education curriculum, China

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