Date
Publisher
arXiv
Artificial intelligence (AI) is increasingly embedded in vocational education systems, yet empirical evidence linking institutional AI readiness to student learning outcomes remains limited. This study develops and tests a 2-2-1 cross-level mediation framework examining how school-level AI readiness is associated with student AI literacy through aggregated teacher mechanisms. Using linked survey data from 1,007 vocational institutions, 156,125 teachers, and 2,379,546 students nationwide, multilevel models were estimated to assess direct, indirect, and contextual effects. Results indicate that overall school AI readiness is positively associated with student AI literacy after adjusting for institutional and regional characteristics. When examined independently, all readiness dimensions show positive associations, while simultaneous modelling suggests that readiness operates as an integrated organisational configuration. Cross-level mediation analyses reveal that aggregated teacher-perceived AI capability partially mediates the relationship between institutional readiness and student literacy, whereas general attitudinal acceptance measures do not demonstrate stable transmission effects. Robustness analyses further show that this readiness-capability-literacy pathway remains structurally stable across heterogeneous regional AI development contexts and under alternative modelling specifications. These findings reposition institutional AI readiness as a multilevel organisational condition linked to student AI literacy, identify collective teacher capability as its central transmission mechanism, and underscore the need to align infrastructural investment with sustained professional capacity development.
Study at a glance
- Question: How is school AI readiness linked to student AI literacy through teachers' capabilities?
- Setting and data: China, national survey across 31 provincial regions, 2025, linked data from 1,007 vocational institutions, 156,125 teachers, and 2,379,546 students.
- Method: Multilevel cross-sectional mediation analysis (2-2-1 cross-level linear mixed-effects modeling) of survey data.
Why it matters
Although policy emphasizes institutional artificial intelligence readiness, empirical evidence directly linking school capacity to student competencies remains limited, leaving unclear how readiness translates into learning outcomes.
Key findings
- Overall school AI readiness was positively associated with student AI literacy after adjusting for institutional and regional characteristics (beta = 0.016).
- Aggregated teacher-perceived AI capability partially mediated the relationship between school AI readiness and student AI literacy, while general teacher acceptance attitudes did not show stable transmission effects.
- The positive association between school readiness, teacher capability, and student literacy remained stable across regions with differing levels of AI industrial development.
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