AI Is Top State Ed-Tech Priority for Second Straight Year as Teacher Training Gap Widens

AI tools ranked as the top ed-tech priority for state K-12 technology leaders for the second consecutive year — but teacher training demand is now growing faster than any school system can fill it, according to new data from Education Week Market Brief.

The finding reveals a structural gap forming underneath the AI deployment surge. Districts are buying and rolling out AI-enabled tools faster than educators can be trained to use them effectively. The result is a wave of tools sitting in classrooms without the professional development needed to realize their value — or, worse, being used in ways that produce unreliable outcomes without educators who can recognize the difference.

What the Data Shows

Education Week Market Brief's new report, based on a survey of K-12 state ed-tech leaders, found:

• 53% of states have now published guidance on evaluating and buying AI-enabled tools, up from 40% a year ago — a 13-percentage-point jump in a single year
• AI ranked first among state ed-tech priorities, the same position it held in 2025
• AI-related professional development emerged as the fastest-growing unmet need in K-12 ed-tech — faster than device procurement, faster than curriculum alignment, faster than data privacy compliance

The gap is structural, not incidental. When purchasing guidance accelerates faster than training infrastructure, tools get deployed into classrooms where teachers don't yet know how to integrate them or evaluate their outputs critically.

Why This Pattern Emerged

The 2025-2026 school year was marked by urgency at the state policy level. After years of debate about whether and how AI belonged in schools, state ed-tech leaders moved toward concrete frameworks: guidelines for procurement, usage policies for students and teachers, and in some cases pilot programs with specific AI products. The result was a 13-point jump in states with formal purchasing guidance.

But purchasing guidance and professional development operate on different timelines. A state can publish a vendor evaluation rubric in months. Training thousands of teachers effectively — with hands-on practice, curriculum integration support, and ongoing coaching — takes years. The acceleration in one created a lag in the other.

There is also a supply problem on the PD side. The organizations and consultants who deliver high-quality AI professional development to schools are a small market relative to the demand now being generated. Districts in rural areas, or those with limited budgets, face the same unmet training need as large urban systems — but with fewer options to address it.

The Risks of Deploying Faster Than Training

When AI tools are in classrooms but teachers aren't trained to use them, several problems compound:

Uncritical use. Students and teachers may treat AI outputs as authoritative rather than as one input among many. Without training in how to evaluate AI claims, fact-check AI-generated content, or recognize when a tool is operating outside its reliable range, the risk of AI-assisted errors in learning increases.

Low adoption. Teachers who don't feel confident with a tool often stop using it after the initial rollout period. High purchase rates followed by low utilization are a well-documented pattern in ed-tech procurement, and AI tools are showing the same trajectory in early adoption data.

Equity gaps. Well-resourced districts with dedicated instructional technology staff can run their own AI professional development programs. Under-resourced districts can't. If PD remains a local responsibility without state or federal support, the gap between high- and low-resource districts in AI-effective teaching will widen — compounding existing inequities in educational technology access.

What States Are and Aren't Doing

The 53% of states with AI purchasing guidance are ahead of where they were a year ago — but purchasing guidance addresses acquisition, not utilization. Few of those same states have published comparably specific requirements for how districts must train teachers before deploying AI tools in classrooms.

Some states have experimented with centralized PD programs — a single state-contracted curriculum that districts can access — but adoption has been uneven. Others have directed federal Title IV-A funds (Student Support and Academic Enrichment grants) toward AI professional development, though those funds were not designed for the volume of training now needed.

The federal picture is also shifting post-midterms. Incoming Democratic committee chairs have signaled interest in AI education broadly, with teacher preparation as a likely component of forthcoming legislation. Whether that produces dedicated funding for AI professional development — or leaves it embedded in broader grant structures where districts must prioritize it against competing needs — will shape whether states can close the training gap.

For districts making purchasing decisions now, the practical implication is clear: buying an AI tool without a concurrent PD plan is not a shortcut. It is a risk that tends to surface as low utilization metrics six months after deployment.

What to Watch

Whether states that have published AI purchasing guidance now follow with mandated training requirements as a condition of district AI tool deployment. And whether the new Democratic Congress includes dedicated AI teacher training funding in forthcoming education technology legislation, or leaves it to states to fund from existing block grants.

By Hector Herrera