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Starting in Washington State, we are building the governance, technical infrastructure, and collaborative norms that enable an ecosystem of AI solution providers to work together — so that learners experience coherent, navigable pathways rather than a patchwork of disconnected tools.
AI companies building pathways solutions each operate in isolation — with their own data models, user experiences, and institutional relationships. Learners encounter a new cold start with every tool.
Market incentives reward data hoarding and feature differentiation over interoperability. Each company maintains incomplete databases with no shared source of comprehensive, accurate information.
As AI tools proliferate in education, no governance mechanism exists that spans the ecosystem to ensure these tools are safe, equitable, and effective for learners — especially minors.
Without shared infrastructure, only the most well-resourced districts and institutions can adopt and integrate new AI tools. The learners who most need coherent support are the last to receive it.
Traditional pathway frameworks are institution-centric — defining pathways as course sequences toward credential achievement, relying on compliance orientation, maintaining weak ties to employment, and depending on overburdened advisors who cannot follow learners beyond program boundaries.
The Learner-Centered Pathways model places the individual at the center of a continuous journey between high school, postsecondary learning, life experiences, and living-wage careers. It provides a shared framework through which multiple AI solutions — each serving different aspects of the journey — can coordinate to deliver a coherent experience rather than a fragmented one.
No single AI tool can serve every aspect of a learner's journey. The model is designed for a diverse ecosystem of specialized solutions that share infrastructure, data, and standards.
When a learner moves between AI solutions — from career exploration to credential planning to financial aid — their context, progress, and goals travel with them. No more cold starts.
A shared infrastructure layer translates systemic complexity into coherent navigation — connecting programs, resources, and human supports across organizational and platform boundaries.
Shared infrastructure lowers barriers to entry for AI developers and lowers costs for states and districts. Every learner gets access to the full ecosystem regardless of where they live.
The pathways landscape requires specialized AI solutions for different aspects of the learner journey. The Pathways AI Project doesn't build these products — we build the shared infrastructure and governance that enables them to work together as a coordinated ecosystem rather than a collection of disconnected tools.
Ecosystem partners are AI companies and nonprofit technology providers who agree to adhere to shared interoperability standards, data governance protocols, and quality benchmarks — ensuring that learners experience seamless transitions across tools and that their data remains private and under their control.
AI solutions that help learners discover career paths aligned with their interests, skills, and labor market realities — connecting aspiration to opportunity.
Tools that help learners evaluate and compare postsecondary credential programs — including cost, duration, completion rates, and employment outcomes.
Solutions that enable learners to carry verified credentials, skills, and learning records across institutions and into the workforce — owned by the learner.
AI platforms connecting job seekers with training and employment opportunities, aligning workforce development with real employer needs and regional labor markets.
Platforms that connect learners with professional mentors and advisors, augmenting human guidance with AI-powered matching and support.
Tools that surface scholarships, grants, basic needs resources, and financial planning support personalized to each learner's circumstances and eligibility.
An open integration protocol that enables different AI-powered tools to share context about a learner's journey — without centralized data storage or compromising privacy. A career exploration tool can understand what credentials a learner has earned through another platform; a mentorship service can understand their academic trajectory — all with appropriate consent and data governance.
Published design patterns that enable participating tools to present a more coherent, navigable experience. When a learner moves between ecosystem platforms, each transition should build confidence rather than compound attrition. Developed through participatory design research with learners who face the greatest navigation burdens.
A structured, continuously updated knowledge graph of supports and credential opportunities — financial aid, scholarships, apprenticeships, credential pathways, mentorship, and support services. Maintained as a shared asset available to all ecosystem partners: no single entity owns or controls this data.
Standards for bias testing, transparency in algorithmic decision-making, and protections against harmful recommendations that all participating ecosystem tools must meet before reaching learners.
Ecosystem-wide protocols that protect learner data while enabling the interoperability that makes the system function. Learner data ownership and consent are non-negotiable foundations.
Benchmarks for the accuracy and currency of information across ecosystem tools — particularly regarding credential value, employment outcomes, and financial aid availability.
Regular audits of participating tools conducted by third-party experts and overseen by state agency partners. A for-profit entity cannot credibly audit its own competitors — a nonprofit can.
The Pathways AI Project will conduct and publish research on the effectiveness of shared infrastructure and ecosystem models in improving learner outcomes, with particular attention to equity implications. We will convene stakeholders to build consensus around interoperability standards and governance norms.
Our research will investigate how AI ecosystems can support — rather than replace — human advising relationships, how shared infrastructure changes the economics of AI adoption for public systems, and how a Learner-Centered Pathways model could be implemented at scale across states.
All research will be designed around the relationships between people, processes, AI systems, and the institutional context in which they operate — because technology initiatives that ignore these dimensions consistently fail.
Open-source knowledge graph framework for modeling learner pathways — available to all ecosystem AI partners
Shared dataset of credential programs, financial aid, career paths, and support resources
Third-party ecosystem-wide safety evaluations with published findings and replicable audit frameworks
Open protocols, reference implementations, and integration guides for AI ecosystem participation
Implementation guidance for states and systems building coordinated AI pathways ecosystems
AI companies, nonprofits, and public agencies will not contribute their data and engineering resources to infrastructure controlled by a competitor. The nonprofit structure ensures shared assets remain shared.
Interoperability reduces switching costs and competitive moats — directly undermining for-profit shareholder value. Only a nonprofit can credibly commit to infrastructure that prioritizes learner outcomes.
AI safety and quality oversight requires independence from the commercial entities being governed. A for-profit entity cannot credibly audit and enforce standards on its own ecosystem.
Public-interest technology infrastructure — which by definition does not generate market-rate returns — is precisely the work philanthropic capital is designed to support.
Washington has the ambition, the infrastructure, and the coalition. If we can build a working model here — with rigorous evaluation, diverse communities, and authentic state partnership — it becomes a blueprint for the nation. Whether you're a state agency, AI developer, or education organization — join us.
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