{"resourceId":"uw-colleague-ai-washington-midyear-2026","versions":[{"version":"external-b1ff4488bd54c3289493bdde6da036eb3c000c0abb84f540d2f38c34c6fc1acb","resource":{"id":"uw-colleague-ai-washington-midyear-2026","title":"UW/Colleague AI midyear report: WA teacher AI use correlated with +0.025 SD reading gains; lesson planning dominates","organization":"University of Washington AmplifyLearn.AI and Colleague AI (IES/NSF-supported)","sector":"K-12 multi-district teacher AI implementation research","geography":"Washington State, United States (12 school districts)","publishedAt":"March 2026 midyear report; arXiv posting April 8, 2026 (arXiv:2605.16277v1)","publicationDate":"2026-04-08","eventDate":"2025-12-31","sourceName":"Generative AI in K-12 Classrooms: A Midyear Implementation Report (arXiv PDF)","sourceLabel":"University–vendor coauthored midyear implementation report with district admin data","sourceUrl":"https://arxiv.org/pdf/2605.16277","evidenceClass":"academic-research","outcomeClass":"mixed","topics":["knowledge-work","accessibility-workforce","operating-model","developers-agents"],"finding":"Across 12 Washington districts, teacher use of Colleague AI grew from 1,438 teachers in September 2025 to 2,891 by end-December (412,304 messages), reaching 4,644 teachers by March 31, 2026 (+61%). Active users averaged ~12 sessions and 3.7 hours; Claire AI lesson-planning features comprised 60–99% of usage in every district. Teachers serving more ELL/SPED students referenced differentiation and accessibility features more often. Among 12,005 students with matched BOY–MOY scores, roughly three hours of teacher AI use associated with a statistically significant +0.025 SD reading interim gain; math association was not distinguishable from zero. Authors label the student-outcome link a preliminary correlation under voluntary adoption.","sledRelevance":"Shows where teacher-facing GenAI actually concentrates (lesson planning) and how modest correlational ELA signals—not math—appear in multi-district U.S. K-12 settings, informing SEA/LEA expectations for staff AI ROI and evaluation design.","evidence":"Opened arXiv PDF end-to-end (executive summary key findings, adoption tables, feature-mix figures, BOY–MOY correlation section). Cross-checked HTML mirror for continuous wording. Confirmed authors’ caution that only a subset of districts provided mid-year admin data and that findings linking use to student characteristics are preliminary signals.","architectureImplications":"District stacks should assume teacher copilots will be lesson-planning heavy; instrument feature telemetry (planning vs delivery vs differentiation) and link roster/SIS identifiers carefully before claiming student-outcome causal effects.","governanceImplications":"Voluntary multi-district pilots need pre-registered outcome metrics and clear disclosure of vendor coauthorship; treat +0.025 SD reading association as a signal to evaluate, not a procurement warranty.","securityPrivacyImplications":"Student-profile and differentiation prompts imply sensitive learner attributes flow into teacher AI sessions—require enterprise DPA, minimization, and audit of profile fields before scaling.","caveats":"Correlational, voluntary adoption, first four months; vendor coauthor (Colleague AI); subset of districts supplied admin data; not an RCT; effect size small; math null. Why now: first unarchived multi-district WA teacher-AI↔student-score package contemporaneous with binding state AI rules and homework-learning-penalty evidence.","streamIds":["k12"],"roles":{"sales":"Sales framing: buyers want teacher-time savings with credible learning signals—not chatbot demos. Lead with lesson-planning telemetry and a bounded reading interim evaluation, and disclose the +0.025 SD association is correlational under voluntary use. Ask whether the district can link teacher AI dosage to interim assessments without exposing student PII to consumer tools. Position a midyear implementation readout like UW’s as the engagement artifact, not guaranteed math gains.","engineering":"Engineering framing: instrument Claire-style planning vs differentiation feature flags, SSO-bound teacher identities, and optional student-profile fields behind consent. Architecture must support dosage metrics (sessions, hours, messages) joinable to interim ELA/math without shipping raw student chat to vendors. Prove ELL/SPED differentiation prompts stay inside the enterprise tenancy and that math-null results will not be buried in marketing dashboards. Build exportable midyear packets districts can defend to boards.","delivery":"Delivery framing: stand up a 12-week teacher AI adoption cohort with PD on lesson planning, capture session/feature telemetry, and run a pre-registered BOY–MOY interim analysis with a published limitations slide. Acceptance is a district-readable midyear report showing adoption curves, feature mix, and any ELA/math associations labeled correlational—plus a decision gate before expanding beyond planning use. Keep vendor research roles disclosed in every briefing."},"roleDetails":{"sales":{"customerProblem":"Districts lack credible, local evidence that teacher GenAI use connects to student learning beyond anecdotal time savings.","stakeholders":["Superintendents","Chief academic officers","CTOs","Teacher leaders","Board research liaisons"],"discoveryQuestions":["Can you link teacher AI dosage to interim ELA/math without consumer tools?","What share of AI use is lesson planning vs student-facing today?","Will you accept correlational midyear signals or require an RCT?","How will ELL/SPED differentiation prompts be governed?"],"valueHypothesis":"Teacher AI that concentrates on lesson planning can show modest correlational ELA signals when dosage and interim scores are measurable—without promising math gains.","potentialEngagement":"8–12 week midyear teacher-AI implementation study: telemetry, PD, and BOY–MOY association analysis with published caveats.","unsupportedClaims":["Causal student learning gains from Colleague AI","Guaranteed math achievement improvement","That +0.025 SD generalizes beyond these WA districts"]},"engineering":{"fit":"Fit for districts with interim assessment data and SSO-managed teacher AI; poor fit where AI use is unmanaged consumer chat.","architectureIntegration":"Teacher IdP → enterprise AI workspace → feature telemetry warehouse → roster join for interim scores; student-profile fields optional and permissioned.","prerequisites":["Teacher SSO and roster data sharing agreement","Interim assessment extracts (BOY/MOY)","Feature-level usage logging","Privacy review for student-profile prompts"],"constraints":["Voluntary adoption selection bias","Vendor coauthorship conflict management","Incomplete admin data from some districts"],"security":"Keep learner attributes and chats in enterprise tenancy; minimize student PII in teacher prompts; audit differentiation feature access.","proofOfValue":["Reproduce adoption counts and feature-mix shares","Replicate +0.025 SD ELA association with district-held data","Show math association remains null under same spec","Document limitations slide matching authors’ caveats"]},"delivery":{"work":"Onboard teacher cohort, enable planning-first workflows, collect dosage telemetry, run interim score joins, and brief leadership on correlational results.","dependencies":["Assessment office","IT/IdP","Union/teacher advisory","Vendor DPA"],"ownership":"CAO owns instructional interpretation; CTO owns telemetry and privacy; principals own classroom practice.","skillsAdoption":"PD focused on lesson-planning prompts and ELL/SPED differentiation; discourage treating AI outputs as student grades.","governanceCheckpoints":["DPA and telemetry plan approved","Pre-registered analysis note","Midyear public limitations briefing","Go/no-go on expansion beyond planning"],"acceptanceCriteria":["Feature-mix report shows planning share by school","ELA/math association table with confidence intervals","Written correlational caveat in board materials","No student-facing GenAI expansion solely on this signal"],"risks":["Selection bias inflates apparent gains","Vendor marketing overclaims causality","Student-profile leakage via differentiation prompts"]}},"retrievedAt":"2026-09-20T03:04:10Z","enrichedAt":"2026-09-20T03:10:38Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Teachers serving more ELL/SPED students already lean on differentiation features—pair AI enablement with accessibility PD and workload-aware adoption supports.","procurementImplications":"Require usage telemetry, interim-outcome evaluation hooks, and disclosure of vendor research roles in RFPs for teacher copilots.","operatingModelImplications":"Treat teacher AI as a planning workflow with midyear evidence reviews, not a perpetual pilot without outcome joins.","sourceVerification":{"openedUrl":"https://arxiv.org/pdf/2605.16277","referenceExcerpt":"This is a preliminary correlation, and the small effect size is an encouraging signal for AI adoption.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}