{"resourceId":"amplify-uw-genai-math-science-teacher-survey-2026","versions":[{"version":"external-99891705814557fad0d1520aca12bacb3ded5962f2f73b36c31008112a005c59","resource":{"id":"amplify-uw-genai-math-science-teacher-survey-2026","title":"National math/science teacher survey finds GenAI adoption rising without workload relief or clear policy","organization":"AmplifyGAIN Center / University of Washington (with WestEd and Colleague AI)","sector":"K-12 education research","geography":"United States (national teacher sample)","publishedAt":"September 2026","publicationDate":"2026-09-01","eventDate":null,"sourceName":"AmplifyLearn.AI / AmplifyGAIN Center","sourceLabel":"IES-supported national teacher survey report (PDF)","sourceUrl":"https://www.amplifylearn.ai/wp-content/uploads/2026/08/rand_yr2.pdf","evidenceClass":"independent-research","outcomeClass":"mixed","topics":["knowledge-work","accessibility-workforce","governance-procurement","operating-model"],"finding":"In a 2026 national survey of U.S. K-12 math and science teachers (N≈1,011), GenAI use for instructional tasks rose to 58% (31% weekly/daily) from 50%/21% in 2025. Use remains mostly teacher-facing—76% of users cite instructional planning—while 74% do not include student GenAI in class. Frequent users report about 45-hour workweeks similar to non-users and spend 7.3 hours/week on lesson planning, 1.4 hours more than non-users. 58% report no formal GenAI training and 42% report no district GenAI guidelines, though formal guidance under development rose to 31% from 18%. Teachers rate student-learning impacts mixed (29% positive, 32% negative) with strongest negativity on academic integrity (60%).","sledRelevance":"Directly informs U.S. district and state K-12 buyers evaluating whether GenAI licenses reduce teacher workload or improve learning. Shows adoption is growing faster than training, policy clarity, and measured student benefit—relevant for procurement, PD design, and operating-model choices that distinguish assisted teacher output from retained student learning.","evidence":"Self-reported survey of public-school math/science teachers fielded via RAND American Teacher Panel–style methods described in the report; 2026 N=1011 unweighted counts in charts; compares 2025 (N=979) to 2026. Associations between usage intensity and time use are correlational, not experimental assignment.","architectureImplications":"Most observed value is teacher-side content drafting rather than student-facing tutoring architecture. Districts should prioritize approved teacher workspaces with logging and DPA controls over uncontrolled general chatbots, and avoid assuming student-facing AI features are the primary adoption path.","governanceImplications":"42% still report no formal district GenAI guidelines and 39% cite unclear guidelines as barriers; policy exposure tracks usage. Districts need written allowed/forbidden uses, student-use rules, and PD before scaling licenses.","securityPrivacyImplications":"Teacher-facing planning tools still ingest prompts that can include student PII if not constrained. Survey does not measure vendor data practices; procurement should require DPAs and no-training clauses independent of self-reported use.","caveats":"Math/science teachers only; self-report bias; usage groups not randomly assigned; no direct student learning outcome measures; Colleague AI co-authorship creates potential interest in teacher AI tools.","streamIds":["k12"],"roles":{"sales":"Sales framing: do not pitch GenAI as automatic teacher time savings. This survey shows frequent users work similar total hours and spend more time planning. Position bounded engagements around clarifying district GenAI policy, mapping allowed teacher workflows, and pairing licenses with role-specific PD—especially lesson planning with academic-integrity guardrails—rather than promising workload reduction or student outcome lifts the sample does not establish.","engineering":"Engineering framing: fit is strongest for controlled teacher-facing drafting inside district-approved tenants, not open student chatbots. Integrate roster-aware auth, prompt logging, and content filters; keep student PII out of planning prompts by default. Prerequisites include DPAs and an approved-tool list before any broad enablement. Proof of value should track planning time, material quality review rates, and student GenAI policy adherence—not license counts alone—while acknowledging survey associations are not causal.","delivery":"Delivery framing: stand up policy + PD before broad rollout. Work includes allowed-use matrices, training on planning workflows, and monitoring whether student GenAI enters classrooms without guidance. Ownership sits with curriculum/instruction and privacy officers jointly. Acceptance criteria: written guidelines published, ≥X% staff trained, and no silent student-facing enablement. Risks include academic-integrity harm and inequitable training access concentrated among already-frequent users."},"roleDetails":{"sales":{"customerProblem":"Districts hear GenAI will cut teacher workload, but leaders lack evidence that adoption reduces hours or improves student learning.","stakeholders":["Chief academic officers","Teacher unions","Curriculum directors","School board members"],"discoveryQuestions":["What share of teachers have formal GenAI training?","Do written district GenAI guidelines exist and are they clear?","Are you measuring planning hours or only license utilization?","Is student GenAI allowed, and how is integrity monitored?"],"valueHypothesis":"A bounded policy-and-PD engagement can align teacher-facing GenAI use with clear rules without claiming unproven time savings.","potentialEngagement":"4–6 week GenAI readiness assessment: survey baseline, guideline draft, PD plan for lesson-planning workflows, and integrity safeguards.","unsupportedClaims":["Guaranteed weekly hours saved","Improved student achievement from teacher planning AI alone","That frequent users already have adequate district policy"]},"engineering":{"fit":"Teacher-facing planning assistants inside approved cloud tenants; weak fit for unsupervised student chatbots given integrity concerns.","architectureIntegration":"SSO to district IdP, approved-model endpoints, prompt/response logging, and optional LMS plugins that do not auto-enable student GenAI.","prerequisites":["Executed DPA","Approved-tool inventory","Role-based access","Acceptable-use policy published"],"constraints":["Math/science survey may not generalize to all subjects","Self-report time measures","Jurisdictional student-data rules"],"security":"Block PII in planning prompts by default; prohibit vendor training on district data; retain audit logs for privacy review.","proofOfValue":["Compare pre/post planning-hour estimates without claiming causality","Sample lesson quality review by instructional coaches","Track % teachers with training and clear guideline awareness"]},"delivery":{"work":"Publish GenAI guidelines, deliver lesson-planning PD, configure approved tools, and define student-use rules with integrity monitoring.","dependencies":["Legal/privacy review","Union consultation where required","Curriculum lead sponsorship","IT tenant configuration"],"ownership":"Curriculum/instruction owns pedagogy; privacy/IT co-own tool approval and logging.","skillsAdoption":"Train on productive planning prompts plus when not to use GenAI; coach review of AI-drafted materials before classroom use.","governanceCheckpoints":["Guideline publication","Tool approval list freeze","PD completion threshold","Semester integrity review"],"acceptanceCriteria":["Written guidelines posted","≥70% targeted teachers complete PD","Student GenAI defaults match policy","Quarterly review of exceptions"],"risks":["Training concentrates among early adopters","Academic integrity degradation","Policy remains unclear despite tool spend"]}},"retrievedAt":"2026-09-17T03:11:01Z","enrichedAt":"2026-09-17T03:16:28Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Adoption without training risks widening gaps between frequent and non-users; PD must reach non-users, not only enthusiasts. Accessibility needs (IEP/504, multilingual) are under-specified in the survey.","procurementImplications":"Buyers should fund training and policy work as first-class line items alongside licenses, and require clarity on student-facing features before purchase.","operatingModelImplications":"Treat GenAI as a teacher workflow change requiring coaching capacity, not a plug-in time saver. Separate teacher-assisted production from student retained learning metrics.","sourceVerification":{"openedUrl":"https://www.amplifylearn.ai/wp-content/uploads/2026/08/rand_yr2.pdf","referenceExcerpt":"Teachers who frequently use GenAI reported spending 7.3 hours in the last week planning and preparing lessons","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}