AI literacy for every student in your state.

Xyplor is purpose-built for K-12 AI literacy at scale. The platform is delivered SaaS, requires no on-premise infrastructure, runs on any browser including Chromebooks, and licenses by enrolled student. State agencies can deploy a single agreement covering multiple LEAs (school districts) with consolidated billing, unified data privacy terms, and a state-level usage dashboard.

ESSA Title IV-A (Student Support and Academic Enrichment Grants) explicitly funds 'effective use of technology' and 'well-rounded education' — both of which AI literacy squarely fits. Title IV-A determinations are made at the LEA level. Xyplor provides alignment documentation; each district's federal programs office makes the final eligibility determination. We also support ESSER residual planning for AI-readiness initiatives.

Xyplor is in early deployment (2026). We do not yet claim ESSA tiered evidence (Tier I-IV). We are partnering with research universities to design longitudinal studies measuring AI fluency development, persistence, and transfer effects. SEAs interested in co-designing evaluation protocols are encouraged to contact us. Our pedagogical model is described in 'Teaching Kids to Direct AI: Four Mechanisms We Built Into Xyplor' on xyplor.org/blog.

We map activities to published state AI literacy frameworks (e.g., California's AI Literacy Framework, Oregon's AI guidance, North Carolina's AI use guidance) on request. We also map to ISTE Standards for Students and CSTA K-12 Computer Science Standards in the AI/ML strand. State agencies issuing new frameworks: we welcome early consultation to ensure alignment.

Student data is encrypted in transit and at rest. Xyplor does not sell student data, does not display third-party advertising, and does not use student content to train AI models. We provide a Data Privacy Agreement (DPA) and sign state-specific data privacy addenda including SDPC NDPA and California SOPIPA disclosures. Data residency in US-based infrastructure (Vercel / AWS regions). Data export and deletion supported on demand.

Three ways. (1) No prior coding required — students with no tech background can start in minutes. (2) Browser-based — works on existing Chromebooks and any device, no app installs. (3) Volume pricing scales with enrollment — small rural LEAs and large urban districts pay the same per-student rate. Multi-language UI is on the roadmap. We can structure agreements to subsidize access in lower-resourced districts.

Yes. State-level master service agreements (MSAs) with LEA-level participation are supported. We can issue per-LEA service orders against a single state agreement, consolidate billing or invoice each LEA separately, and provide a state-level dashboard showing aggregate usage, equity metrics, and outcome indicators across participating LEAs.

Our position: kids must learn to *direct* AI, not be replaced by it. We teach four core skills — describing intent in natural language, evaluating AI output, iterating with specific feedback, and exercising judgment about what to use. These transfer to every AI-augmented context students will encounter. We do not promote AI as a homework-doer or ghostwriter; AI conversations are parent and educator visible by design.

Three pathways: (1) An information call with the founder to discuss your state's AI priorities. (2) A pilot agreement covering a small set of LEAs to validate fit. (3) A full state-level MSA. We are actively in conversations with state AI advisory groups and are happy to brief working groups, attend SEA convenings, or provide written input to state AI strategy documents.

Xyplor was founded by Vinay Abburi, a technology and engineering leader with experience scaling B2B and consumer technology. Xyplor is independent (not subsidiary or platform-owned). Our pedagogical thinking is publicly documented and CC BY 4.0 licensed at xyplor.org/blog. We engage transparently with educators, researchers, and policy-makers shaping the AI-in-education conversation.