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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