Source: Dusseault, B., Sims, M., & Berardino, M. (2025). AI early adopter districts: The promises and challenges of using AI to transform education. Center on Reinventing Public Education. https://files.eric.ed.gov/fulltext/ED674608.pdf
LEARN Brief Credit: Dr. Jeannie Haubert
Overview
This is brief 3 of 4 in a comprehensive national study of AI adoption at the district level. Even the most committed Early Adopter districts in CRPE’s national study are navigating AI adoption without a clear roadmap. Data privacy concerns, unstable funding, an overwhelming and aggressively-marketed vendor landscape, and inconsistent (or nonexistent) state guidance are combining to stall momentum — right as ESSER funding disappears and subscription costs pile up. Superintendents can’t single-handedly fix state policy or the vendor market, but they can control how their own district evaluates, contracts for, and budgets for AI tools, rather than being swept along by whoever pitches the loudest.
Key Insights
- Data privacy and sustainable funding are the two most-cited barriers, each flagged as “considerable” or “extreme” difficulty by roughly a third of surveyed district leaders. Ongoing subscription fees and device refresh cycles are now colliding head-on with the fiscal cliff created by expiring ESSER funds, making long-term AI investment financially precarious for many districts.
- Roughly 3 in 10 leaders cite a lack of internal capacity and uneven state guidance as major barriers, and the gap is stark: 80% of districts in states without any state-level AI guidance called that absence “extremely difficult,” compared to a much smaller share in states that have issued clear frameworks.
- Aggressive vendor marketing and the sheer pace of new product releases are driving genuine decision paralysis, leaving many districts to default to whatever tool is best-marketed and most accessible rather than best-evidenced — often without a trusted, independent way to vet instructional value, data protections, or long-term viability.
- Interoperability is a quieter but equally damaging barrier: outdated data systems and siloed AI tools that can’t “talk” to Student Information Systems or Learning Management Systems leave districts “data-rich but information-poor” — unable to connect what different platforms know about a student into one usable picture for teachers or families.
- Not every district is stuck reacting. “System Changers” and “Reimaginers” are flipping the dynamic — building direct relationships with edtech providers and local industry partners so they help shape how tools are built, rather than simply accepting whatever lands in their inbox. As one cabinet leader put it, if districts don’t help solve this, someone else will solve it for them.
Action Steps
- Stand up a formal AI vendor-vetting and procurement process — including data-privacy and contractual standards — before your next sales pitch arrives, rather than negotiating each contract from scratch under pressure.
- Budget AI tools as recurring operating costs in your long-term financial plan, not as one-time, grant-funded projects; start planning now for the post-ESSER funding cliff.
- If your state has not issued AI guidance, advocate directly to your state education agency — districts in guidance-free states report dramatically higher difficulty navigating adoption alone.
- Make interoperability with your SIS and LMS a non-negotiable requirement in every new AI contract, and require vendors to document data flows clearly enough for your team to evaluate quickly.
- Seek out a trusted, shared vetting mechanism — state-provided, third-party, or a peer consortium — so no single district has to evaluate the entire vendor market alone.
Full Article
Read the full CRPE report: https://files.eric.ed.gov/fulltext/ED674608.pdf
