Ecological Predictors of AI Literacy in Chinese K-12 Teachers: A Structural Equation Modeling Study
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| Title: | Ecological Predictors of AI Literacy in Chinese K-12 Teachers: A Structural Equation Modeling Study |
|---|---|
| Language: | English |
| Authors: | Xiaofan Wu, Nagaletchimee Annamalai |
| Source: | Electronic Journal of e-Learning. 2026 24(2):47-60. |
| Availability: | Academic Conferences Limited. Curtis Farm, Kidmore End, Nr Reading, RG4 9AY, UK. Tel: +44-1189-724148; Fax: +44-1189-724691; e-mail: info@academic-conferences.org; Web site: https://academic-publishing.org/index.php/ejel/index |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Elementary Education Secondary Education |
| Descriptors: | Artificial Intelligence, Digital Literacy, Foreign Countries, Elementary School Teachers, Secondary School Teachers, Predictor Variables, Ecological Factors, Teacher Characteristics, Technology Uses in Education |
| Geographic Terms: | China |
| ISSN: | 1479-4403 |
| Abstract: | Although AI is being rapidly developed and applied in education, gaps remain in factors affect teachers' AI literacy. A cross-sectional survey of 1,680 teachers was conducted to explore relationships between school environment, social environment, teacher self-efficacy, and AI literacy via structural equation modeling (CFI = 0.986; RMSEA = 0.03). The results showed that teachers' AI literacy was 3.89 ± 1 (out of 5) in total, and the theory-practice gap was significant: stronger performance in awareness (β = 0.75) and ethics (β = 0.76), but weaker performance in application literacy (β = 0.72) and evaluation literacy (β = 0.81). School environment had the strongest direct effect on AI literacy (β = 0.270, p < 0.001), followed by teacher self-efficacy, which served as an important mediator (β = 0.259, p < 0.001). Social environment had no direct effect on teachers' AI literacy (β = 0.060, p = 0.362), implying that distal effects need to be mediated by school. Demographic analysis showed urban--rural differences, decline after age 40, and subject differences (science > liberal arts). Therefore, we suggest that policymakers should transfer to supporting school-level interventions with targeted resources allocation. School leaders should create supportive technological environments and self-efficacy programs. In addition, teachers should participate in hands-on training with a focus on practical skills. This study provides useful references for integrating AI into K-12 education in China. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1504759 |
| Database: | ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: Ecological Predictors of AI Literacy in Chinese K-12 Teachers: A Structural Equation Modeling Study – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiaofan+Wu%22">Xiaofan Wu</searchLink><br /><searchLink fieldCode="AR" term="%22Nagaletchimee+Annamalai%22">Nagaletchimee Annamalai</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Electronic+Journal+of+e-Learning%22"><i>Electronic Journal of e-Learning</i></searchLink>. 2026 24(2):47-60. – Name: Avail Label: Availability Group: Avail Data: Academic Conferences Limited. Curtis Farm, Kidmore End, Nr Reading, RG4 9AY, UK. Tel: +44-1189-724148; Fax: +44-1189-724691; e-mail: info@academic-conferences.org; Web site: https://academic-publishing.org/index.php/ejel/index – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 13 – Name: DatePubCY Label: Publication Date Group: Date Data: 2026 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+Literacy%22">Digital Literacy</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+School+Teachers%22">Elementary School Teachers</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Teachers%22">Secondary School Teachers</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+Factors%22">Ecological Factors</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Characteristics%22">Teacher Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink> – Name: Subject Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: ISSN Label: ISSN Group: ISSN Data: 1479-4403 – Name: Abstract Label: Abstract Group: Ab Data: Although AI is being rapidly developed and applied in education, gaps remain in factors affect teachers' AI literacy. A cross-sectional survey of 1,680 teachers was conducted to explore relationships between school environment, social environment, teacher self-efficacy, and AI literacy via structural equation modeling (CFI = 0.986; RMSEA = 0.03). The results showed that teachers' AI literacy was 3.89 ± 1 (out of 5) in total, and the theory-practice gap was significant: stronger performance in awareness (β = 0.75) and ethics (β = 0.76), but weaker performance in application literacy (β = 0.72) and evaluation literacy (β = 0.81). School environment had the strongest direct effect on AI literacy (β = 0.270, p < 0.001), followed by teacher self-efficacy, which served as an important mediator (β = 0.259, p < 0.001). Social environment had no direct effect on teachers' AI literacy (β = 0.060, p = 0.362), implying that distal effects need to be mediated by school. Demographic analysis showed urban--rural differences, decline after age 40, and subject differences (science > liberal arts). Therefore, we suggest that policymakers should transfer to supporting school-level interventions with targeted resources allocation. School leaders should create supportive technological environments and self-efficacy programs. In addition, teachers should participate in hands-on training with a focus on practical skills. This study provides useful references for integrating AI into K-12 education in China. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: EJ1504759 |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 47 Subjects: – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Digital Literacy Type: general – SubjectFull: Foreign Countries Type: general – SubjectFull: Elementary School Teachers Type: general – SubjectFull: Secondary School Teachers Type: general – SubjectFull: Predictor Variables Type: general – SubjectFull: Ecological Factors Type: general – SubjectFull: Teacher Characteristics Type: general – SubjectFull: Technology Uses in Education Type: general – SubjectFull: China Type: general Titles: – TitleFull: Ecological Predictors of AI Literacy in Chinese K-12 Teachers: A Structural Equation Modeling Study Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiaofan Wu – PersonEntity: Name: NameFull: Nagaletchimee Annamalai IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1479-4403 Numbering: – Type: volume Value: 24 – Type: issue Value: 2 Titles: – TitleFull: Electronic Journal of e-Learning Type: main |
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