July 28, 2026

Advocate Resource Guide: Ways to Push for High-Quality AI-Powered Ed Tech

By Marisa Mission, Brian Robinson, Mark Baxter

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#3 IN THE SERIES

Welcome to the “Signals of Quality” series — a look at what separates genuinely effective AI-powered ed tech from flashy technology, and how state leaders and advocates can build procurement practices that put student learning first. Given the growing skepticism of artificial intelligence (AI) and ed tech, how should K-12 education leaders think about what “good” tools look like? Safety-oriented frameworks for AI tools abound, but little exists on assessing efficacy and quality for AI tools used by students and teachers in classrooms. Building on early research, this series 1) surfaces five signals of quality in AI-powered tools, and 2) identifies concrete steps state leaders and advocates can take to prioritize efficacy and learning throughout a procurement ecosystem. These insights arise from the AI Policy Hub, a partnership between Bellwether and PIE Network to connect advocates with resources, support, and national education experts.

Procurement (the process of sourcing, negotiating, and acquiring tools) is one of the most powerful ways education leaders can shape how students interact with AI tools in the classroom. This includes not just state and district leaders but also advocates, who play a critical role in ensuring that states and districts make investments that drive growth in student learning. Specifically, advocates can:

Inform conversations with a range of stakeholders from policymakers to parents,
Identify where current procurement practice falls short, and
Ask sharper questions as policies and guidance evolve.

In our final installment of Signals of Quality, we connect the five quality signals identified earlier in the series to specific actions advocates can take to support stronger AI procurement ecosystems.

Quality Signal	How Advocates Can Act on This Signal
Signal 1: Emphasis on learning outcomes, not technology features.	Help leaders align on priority outcomes before they embark on procurement. Advocates can serve as a key resource to connect state and district leaders with critical stakeholders to understand how technology investments improve important outcomes, whether those are academic mastery, durable skills, student behavior or well-being, teacher development, etc. By making these targets explicit, advocates can ensure that district or state leaders anchor the review process on those success metrics. Bring familiar evidence frameworks into AI conversations. Advocates can point to existing benchmarks such as the Every Student Succeeds Act’s Tiers of Evidence framework, so that AI tools are held to the same standards of evidence as other interventions. Support public evaluation infrastructure to accelerate developers’ research. Robust evidence bases will make districts’ and states’ choices easier. By accessing and leveraging initiatives such as Digital Promise’s K-12 AI Infrastructure Program and the evolving Learning Commons work, advocates can connect key stakeholders, elevate marginalized voices, and disseminate trustworthy information. Keep attention on the students for whom AI tools might fail. Algorithmic bias is a well-documented risk of machine learning. Early research on generative AI shows worse performance for multilingual learners, and limited evidence exists on whether AI tools perform well for students with disabilities or students performing below grade level. Advocates should push for systemic safeguards against low-quality tools, such as outcomes-based contracts that require disaggregated analyses, making a tool’s performance visible across student groups rather than hidden in an overall average.
Signal 2: Productive struggle as a primary pathway for learning.	Support and empower stakeholders to ask questions. When parents, educators, procurement officers, or advocates raise the same questions, it sends a clear demand signal to states, districts, and vendors for transparency and alignment. While some may feel intimidated by the technical aspects of ed tech, advocates can ask and expect straightforward answers to questions such as: What specific instructional problem does this tool solve? States, districts, and vendors should be able to explain more than just the technology (e.g., “this tool builds student fluency in math by identifying misconceptions using AI” rather than “uses generative AI to personalize learning”). How does this tool preserve productive struggle and student agency? States, districts, and vendors should be able to provide specific examples of guardrails against instant-answer behavior, over-scaffolding, or bypassing student effort.
Signal 3: Sound pedagogy and coherence with existing instructional practice.	Connect AI tool quality to existing instructional initiatives rather than treating it as a separate track. For example, many states and districts already have policies around adopting high-quality instructional materials; advocates involved in those efforts can push for aligned AI tools by asking questions such as: What evidence exists for a tool’s pedagogical strategy? For example, many developers choose feedback for students as their tool’s primary instructional mechanism, drawing on research that shows timely, specific, and actionable feedback drives skill development. By making connections to existing evidence, advocates can help stakeholders better understand a tool’s quality and value. Where does this fit in the classroom? Is the primary user a teacher or student? Is it used as practice, as part of introducing a concept, or as homework? Is it intended to complement or supplement a teacher’s expertise? The answers reveal whether a vendor has thought carefully about instructional coherence.
Signal 4: Technical configurations designed to maximize quality.	Support capacity building and collaboration to boost states’ and districts’ technical expertise. Developing internal capacity now will pay off in stronger procurement decisions over time and reduced reliance on external review. Advocates can encourage investments and facilitate collaborative efforts across districts and states through consortia, communities of practice, or technical advisory committees. Translate technical terms for a wider audience. While vendors should be able to explain their tools’ design and trade-offs, it is equally important for consumers and advocates to build AI literacy. Advocates who have worked on education data systems or privacy policies can apply their technical expertise to AI tools and translate key concepts to help other stakeholders engage thoughtfully and substantively.
Signal 5: Attention to market sustainability and long-term planning.	Encourage procurers to raise vendor stability early, before a contract is signed. Advocates can press for financial sustainability and contingency planning to be part of the conversation from the start: What is a vendor’s current financial status, including revenue and expenses to date? What happens to services, data, and access to the tool’s outputs if it exits the market mid-contract? Surfacing these questions early prevents leaders from discovering the risk too late. Push for data ownership and transfer clauses to be a default. AI tools generate new artifacts (e.g., student- or teacher-AI interaction logs or inferred data about the district’s student population) that are increasingly valuable for evaluating tools, training future models, and informing instructional decisions. Advocates can help ensure vendor contracts include language guaranteeing data are owned by and transferred to the state or district when the contract expires.

Three Steps to Get Started

The signals above describe what quality can look like and how advocates can support procurement decisions that prioritize it. Separately, the right starting point depends on an organization’s context, relationships, and existing priorities. Below are three entry points for advocacy organizations looking to get started in this arena.

Identify which dimensions of quality for AI tools matter most to your organization and community. The five signals above may not carry equal weight in all contexts, and advocates may need to prioritize. Those focused on equity might emphasize outcomes-based contracts and disaggregated evidence; others worried about a churning vendor market might start with sustainability and data portability. Naming priorities first creates natural next steps, including knowing what to look for and which gaps matter.
Take stock of the current state of procurement guidance for AI tools in your community. Identify whether your state recognizes any third-party certifications (independent assessments of AI tools), maintains a list of vetted options, or has issued AI-specific guidance. At the local level, determine what AI tools are currently in use, how they were procured, and what review processes (if any) are used to evaluate quality post-procurement. Ask whether existing contracts include contingency provisions for vendor instability, disclosure requirements, or data ownership and portability clauses that cover AI-generated artifacts. Document where gaps exist, relative to your priorities.
Monitor other states or districts for approaches relevant to your local context. For example, California’s forthcoming AI vendor certification standards are likely the first in a wave of efforts to regulate procurement, and more quality-oriented frameworks may follow soon. Resources such as PIE Network’s AI Policy Hub can summarize developments for easier tracking.

The field’s thinking on what makes an AI tool high-quality is still early, and much of today’s procurement infrastructure was built for a slower-moving market. As state guidance and procurement practice continue to evolve, advocates will play a key role in ensuring that students get AI tools that genuinely serve them.

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