{"resourceId":"rand-school-guidance-gap","versions":[{"version":"legacy/2026-08-27/rand-school-guidance-gap","resource":{"id":"rand-school-guidance-gap","title":"School AI use grows faster than policy and professional learning","organization":"RAND Corporation","sector":"K–12 education","geography":"United States","publishedAt":"September 30, 2025","sourceName":"AI Use in Schools Is Quickly Increasing but Guidance Lags","sourceLabel":"RAND RRA4180-1","sourceUrl":"https://www.rand.org/pubs/research_reports/RRA4180-1.html","evidenceClass":"independent-research","outcomeClass":"mixed","topics":["knowledge-work","data-security","accessibility-workforce","operating-model"],"finding":"Nationally representative panels found rapidly growing AI use among students and teachers while training, school policy, and shared expectations remained uneven.","sledRelevance":"Districts need operational guidance distinguishing instructional uses, student support, assessment integrity, accessibility, privacy, and staff responsibilities.","evidence":"Fifty-four percent of students and 53% of core-subject teachers reported using AI in 2025, each increasing by more than 15 percentage points. Training and policy lagged while stakeholder risk perceptions diverged.","architectureImplications":"Use an approved-tool catalog with identity, age-appropriate access, accessibility, integration, logging, and data minimization requirements.","governanceImplications":"Align acceptable-use rules, professional learning, assessment guidance, procurement, family communication, and outcome review at the district level.","securityPrivacyImplications":"Apply student privacy law, parental and age safeguards, vendor data-use limits, retention controls, and non-AI access pathways.","caveats":"Survey responses describe reported behavior and perceptions, not causal effects on learning or teacher productivity."}},{"version":"enrichment/2026-09-05T02:33:27.019Z/rand-school-guidance-gap","resource":{"id":"rand-school-guidance-gap","title":"School AI use grows faster than policy and professional learning","organization":"RAND Corporation","sector":"K–12 education","geography":"United States","publishedAt":"September 30, 2025","publicationDate":"2025-09-30","eventDate":null,"sourceName":"AI Use in Schools Is Quickly Increasing but Guidance Lags","sourceLabel":"RAND RRA4180-1","sourceUrl":"https://www.rand.org/pubs/research_reports/RRA4180-1.html","evidenceClass":"independent-research","outcomeClass":"mixed","topics":["knowledge-work","data-security","accessibility-workforce","operating-model"],"finding":"Nationally representative panels found rapidly growing AI use among students and teachers while training, school policy, and shared expectations remained uneven.","sledRelevance":"Districts need operational guidance distinguishing instructional uses, student support, assessment integrity, accessibility, privacy, and staff responsibilities.","evidence":"Fifty-four percent of students and 53% of core-subject teachers reported using AI in 2025, each increasing by more than 15 percentage points. Training and policy lagged while stakeholder risk perceptions diverged.","architectureImplications":"Use an approved-tool catalog with identity, age-appropriate access, accessibility, integration, logging, and data minimization requirements.","governanceImplications":"Align acceptable-use rules, professional learning, assessment guidance, procurement, family communication, and outcome review at the district level.","securityPrivacyImplications":"Apply student privacy law, parental and age safeguards, vendor data-use limits, retention controls, and non-AI access pathways.","caveats":"Survey responses describe reported behavior and perceptions, not causal effects on learning or teacher productivity.","streamIds":["k12"],"roles":{"sales":"Interpretation — Customer problem: reported student and teacher adoption can outrun consistent district rules and professional learning. Stakeholders: curriculum, assessment, teacher development, IT, privacy, accessibility, student services, and family communications. Discovery: which tools and tasks are already used; where do classroom rules conflict; who lacks training or access; and how are approved uses communicated? Value hypothesis: coherent guidance and an approved-tool process may reduce uncertainty and support responsible participation. Potential engagement: district practice assessment, policy alignment, and role-specific training tied to actual classroom scenarios. Unsupported claims: RAND's reported 54% student and 53% teacher use establishes survey behavior, not improved learning, teacher productivity, or the adoption rate in a particular district.","engineering":"Interpretation — Fit: support district-approved instructional and staff tools with controls appropriate to their users and tasks. Architecture and integration: connect the tool catalog to identity, age-appropriate access, accessibility, logging, and existing learning workflows; preserve a non-AI path. Prerequisites: agreed acceptable-use and assessment rules, a data inventory, and vendor prompt/output handling terms. Constraints: different teacher and student needs require distinct permissions and guidance, while inconsistent classroom practices may defeat technical defaults. Security: minimize student data, evaluate retention and vendor data use, and verify age and parental safeguards with responsible district staff. Proposed validation: test representative teacher, student, and accessibility scenarios against the district's policy, checking authorization, prohibited inputs, and usable alternatives before broader enablement.","delivery":"Interpretation — Work: reconcile classroom guidance, review the tool catalog, deliver professional learning, and communicate expectations to students and families. Dependencies: curriculum and assessment decisions, approved vendor terms, accessible devices, and time for staff training. Ownership: district curriculum leads instructional rules; IT operates access; privacy/accessibility owners approve safeguards; principals and teachers apply task-level guidance. Skills and adoption: use classroom examples to practice disclosure, output review, and non-AI options. Governance checkpoints: policy approval, tool onboarding, and periodic review of exceptions and outcomes. Proposed acceptance: sampled classrooms communicate consistent core rules, staff demonstrate approved data handling, and access, assessment validity, and student experience are measured. Risks include policy existing only on paper and confusing growing use with educational effectiveness."},"retrievedAt":null,"enrichedAt":"2026-09-05T02:33:27.019Z","enrichmentBasis":"archived evidence"}}]}