From School AI Readiness to Student AI Literacy: A National Multilevel Mediation Analysis of Institutional Capacity and Teacher Capability
- URL: http://arxiv.org/abs/2603.20056v1
- Date: Fri, 20 Mar 2026 15:38:30 GMT
- Title: From School AI Readiness to Student AI Literacy: A National Multilevel Mediation Analysis of Institutional Capacity and Teacher Capability
- Abstract summary: 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.<n>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.
- Score: 7.400138068424031
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Faculty Orientations Shape Adoption of AI in Research and Teaching [0.0]
This study examined relationships between AI use, attitudes, institutional context, and instructional practice.<n>Results suggest that existing technology-adoption models may not fully explain adoption in contexts where technologies interact directly with disciplinary reasoning and knowledge production.
arXiv Detail & Related papers (2026-05-18T09:47:35Z) - Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues [46.915280360954576]
We propose an interpretable difficulty-aware conversational KT framework built upon large language models (LLMs)<n>The framework incorporates the original textual question and the next tutor-posed task to estimate the student's knowledge state and the difficulty of the upcoming turn.<n>We evaluate the framework on two tutor-student dialogue datasets.
arXiv Detail & Related papers (2026-05-01T21:07:24Z) - Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning [7.3864557762003615]
This paper reframes student AI use in assessment as a coordination problem shaped by peer expectations and assessment design.<n>We use analytical results and finite-population simulations to reveal threshold-driven behavioural transitions in student AI use.
arXiv Detail & Related papers (2026-04-23T21:21:14Z) - A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms [49.66022971508878]
We argue that reasoning should be elevated from a modular component to the system's cognitive core.<n>We derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning.<n>We identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control.
arXiv Detail & Related papers (2026-03-11T07:40:53Z) - Epistemology gives a Future to Complementarity in Human-AI Interactions [42.371764229953165]
complementarity is the claim that a human supported by an AI system can outperform either alone in a decision-making process.<n>We argue that historical instances of complementarity function as evidence that a given human-AI interaction is a reliable process.
arXiv Detail & Related papers (2026-01-14T21:04:28Z) - How to Assess AI Literacy: Misalignment Between Self-Reported and Objective-Based Measures [6.943056322842481]
The widespread adoption of Artificial Intelligence (AI) in K-12 education highlights the need for psychometrically-tested measures of teachers' AI literacy.<n>This study developed and evaluated SR and OB measures of teacher AI literacy within the established framework of Concept, Use, Evaluate, and Ethics.<n>Latent profile analysis identified six distinct profiles, including overestimation (SR > OB), underestimation (SR OB), alignment (SR close to OB) and a unique low-SR/low-OB profile among teachers without AI literacy experience.
arXiv Detail & Related papers (2026-01-03T00:10:41Z) - Designing AI-Resilient Assessments Using Interconnected Problems: A Theoretically Grounded and Empirically Validated Framework [0.0]
The rapid adoption of generative AI has undermined traditional modular assessments in computing education.<n>This paper presents a theoretically grounded framework for designing AI-resilient assessments.
arXiv Detail & Related papers (2025-12-11T15:53:19Z) - Academics and Generative AI: Empirical and Epistemic Indicators of Policy-Practice Voids [0.0]
This study prototypes a ten-item, indirect-elicitation instrument embedded in a structured interpretive framework to surface voids between institutional rules and practitioner AI use.
arXiv Detail & Related papers (2025-11-04T06:24:47Z) - Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles [41.419459280691605]
Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT.<n>This scoping review aims to examine how the AIES and FAccT communities conceptualize, measure, and validate AI trustworthiness.
arXiv Detail & Related papers (2025-10-24T09:40:38Z) - The AI Literacy Heptagon: A Structured Approach to AI Literacy in Higher Education [41.99844472131922]
The study aims to bridge the gap between theoretical AIL conceptualizations and the practical implementation in academic curricula.<n>Our analysis identifies seven central dimensions of AIL: technical, applicational, critical thinking, ethical, social, integrational, and legal.
arXiv Detail & Related papers (2025-09-23T11:28:30Z) - AI-Educational Development Loop (AI-EDL): A Conceptual Framework to Bridge AI Capabilities with Classical Educational Theories [8.500617875591633]
This study introduces the AI-Educational Development Loop (AI-EDL), a theory-driven framework that integrates classical learning theories with human-in-the-loop artificial intelligence (AI)<n>The framework emphasizes transparency, self-regulated learning, and pedagogical oversight.
arXiv Detail & Related papers (2025-08-01T15:44:19Z) - (AI peers) are people learning from the same standpoint: Perception of AI characters in a Collaborative Science Investigation [0.0]
scenario-based assessment (SBA) introduces simulated agents to provide an authentic social-interactional context.<n>Recent advancements in multimodal AI, such as text-to-video technology, allow these agents to be enhanced into AI-generated characters.<n>This study investigates how learners perceive AI characters taking the role of mentor and teammates in an SBA mirroring the context of a collaborative science investigation.
arXiv Detail & Related papers (2025-06-06T15:29:11Z) - Methodological Foundations for AI-Driven Survey Question Generation [41.94295877935867]
This paper presents a methodological framework for using generative AI in educational survey research.<n>We explore how Large Language Models can generate adaptive, context-aware survey questions.<n>We examine ethical issues such as bias, privacy, and transparency.
arXiv Detail & Related papers (2025-05-02T09:50:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.