Building Human Infrastructure to Mitigate AI Fairness Harms in K-12 Education
The rapid introduction of tools powered by artificial intelligence (AI) in K-12 education offers promises of data-driven personalized learning, real-time feedback, and relief for educators’ overstretched workloads. However, increasing access to emerging technologies alone is insufficient for achieving this vision. Without sustained, high-quality professional learning (PL), AI risks deepening a “digital design divide“— a gap where educators lack the support necessary to transform learning experiences by leveraging technology responsibly and effectively.
This challenge is not new. It mirrors a long-standing phenomenon in K-12 education where significant technology acquisitions occur without due efforts to sustainably build educator capacity. To mitigate this risk, state legislatures and education agencies must prioritize investments in human infrastructure– especially teachers, moving beyond systems that prioritize short-term tool training toward durable, high-quality professional learning systems.
Challenge and Opportunity
While a majority of U.S. educators now use AI in their work, the necessary support to use these tools effectively and responsibly lags significantly. According to RAND, half of the nations’ school districts have not provided training on AI, and high-poverty districts are even less likely to have provided training compared to their low-poverty counterparts. The failure to provide this essential support and the resulting disparity poses a dual fairness risk for vulnerable student groups. They may be subjected to biased or harmful AI practices, and they are also more likely to miss out on the innovative uses of AI, including deeply personalized learning responsive to their strengths, backgrounds, experiences, prior knowledge, and needs.
Furthermore, recent research identifies four systemic issues in current systems that govern professional learning (PL) for high-quality, technology-enabled instruction:
- Inconsistent Definitions: While K-12 leaders share a vision for student-centered instruction where technology like AI deepens and accelerates learning, few have a formal definition of what this looks like in practice or how PL supports it.
- Short-Term Funding Defaults: Funding often targets “one-off” workshops for specific tools and platforms. These meet immediate rollout needs but fail to build durable educator capacity in a rapidly evolving landscape.
- Compliance-Driven Monitoring: Tracking the impact of PL efforts often focuses on box-checking for relevant laws and regulations, rather than using data to refine instructional strategy.
- Lack of Documented Models: State leaders struggle to synthesize and disseminate locally-initiated, well-documented PL programs with evidence of success.
The real opportunity of AI lies not just in the tools, but in an educator workforce prepared to wield them. High-quality PL must thus move beyond short-term tool training to focus on areas necessary for equitable implementation, such as AI fairness and bias mitigation, ethical use of data, critical thinking, data foundations, and deep integration of AI-enabled tools into standards-aligned, high-quality instruction. When done right, this investment in human infrastructure ensures AI accelerates learning outcomes for all students, closing the “digital design divide.”
State legislatures and education agencies are pivotal actors who must address this issue through strategic policy levers. While individual districts manage much of the budget implementation and programmatic decisions, states set the conditions for local success by aligning funding streams and defining clear instructional visions.
Plan of Action
Recommendation 1. Define and Promote Aligned Visions of AI-Enabled Instruction
- Establish Definitions: Led by the state education agency and developed with broad input from stakeholders (e.g., educators, students, families, industry, research), publish and embed a statewide definition of high-quality, AI-enabled instruction in existing guidance and policy. For instance, states have leveraged frameworks like the ISTE Standards, CSTA Learning Priorities, and Universal Design for Learning guidelines to inform their definitions.
- Align Funding Streams: Require districts applying for PL funds, including the $2 billion Title II-A program under the Elementary and Secondary Education Act (ESEA), to describe how their proposed activities align with the state’s articulated vision.
- Provide Exemplars: Curate sample classroom artifacts and PL tools that reflect the vision.
Recommendation 2. Align Funding With Instructional Priorities
- Name Allowable Uses: Explicitly list AI and digital learning supports as allowable uses in guidance for various PL funding streams.
- Ensure Deliberate Procurement: Develop guidelines that encourage the procurement of safe, evidence-based, inclusive, usable, and interoperable tools, as well as risk and impact assessments for AI-powered edtech.
- Synchronize Cycles: Align grant cycles and reporting deadlines so districts can plan coherent, multi-year PL investments.
Recommendation 3. Leverage Compliance Structures for Continuous Improvement
- Shift Monitoring Metrics: Create systems that prompt districts to report not only educator attendance in PL efforts, but also shifts in practice and student impact, with a focus on non-punitive, streamlined data collection to minimize administrative burden.
- Collaborative Coaching: Use monitoring meetings as coaching sessions in addition to compliance audits, providing clear, actionable feedback to improve PL activities.
Recommendation 4. Encourage Durable Professional Learning Models
- Prioritize Sustainable Roles: Structure funding to prioritize ongoing coaching networks and dedicated AI readiness specialists over standalone training.
- Regional Infrastructure: Partner with regional education service centers to provide shared coaching staff across multiple districts.
- Elevate Educator Voice: Encourage PL programs establish formal mechanisms for teacher input in design, iteration, and evaluation to ensure practical relevance and quality.
Recommendation 5. Work Across Silos in State Leadership
- Cross-Departmental Collaboration: Establish regular touchpoints between key staff—specifically federal programs directors (e.g., ESEA Title program leads), curriculum leads, and edtech leaders—to align priorities and ensure funding streams work in concert.
- Joint Guidance and Pilots: Publish guidance that connects AI literacy directly to curriculum standards and assessment strategies. Launch pilot projects that combine resources from multiple departments, such as blending AI literacy PL with new project-based learning initiatives, and evaluate them together.
Recommendation 6. Document, Highlight, and Scale What Works
- State Repositories: Launch an online hub or in-person showcases where districts can share artifacts of model PL activities and road maps for implementing effective coaching initiatives.
- Responsible Innovation Amplification: Create awards or public recognition programs that spotlight measurable progress in AI-enabled instruction, helping to inspire others.
State education agencies specifically can adapt these recommendations based on their current capacity and context. For example:
- Low-Intensity
- Explicitly note across existing guidance documents (e.g., ESEA Title program funding guidance) that federal and state funds can support PL tied to AI-enabled instruction.
- Curate aligned PL examples to model high-quality investments into human infrastructure.
- Medium-Intensity
- Publish a statewide vision for AI-enabled instruction and responsible AI use, and integrate this vision into grantmaking processes.
- Convene alignment workshops and develop sample braided budgets that connect various streams of funding.
- Create and disseminate a high-quality PL model on AI fairness and use for optional adoption by districts, encouraging delivery through local or regional coaching networks for maximum relevance and sustainability
- High-Intensity
- Codify durable PL structures in policy, including by embedding roles like instructional technology coaches in state regulations or quality standards, prioritizing the development of internal educator capacity and clear teacher-to-coach career pathways.
- Institutionalize cross-departmental collaboration by establishing offices or leadership teams that coordinate curriculum, assessment, technology, and professional learning.
Conclusion
According to SETDA’s edtech trends survey, AI is currently the leading state edtech priority and top state initiative. However, with only a small group of states currently prioritizing existing funds for technology training, there is an immediate need to improve the systems governing professional learning. By investing in the “human infrastructure,” as exemplified by states like Wyoming and Massachusetts, state leaders can ensure that AI becomes a tool for accelerating outcomes for all students.
Soft law was never meant to be a permanent solution. Treating it as one, and letting the sandcastle stand in for the skyscraper indefinitely, is how we end up with a decade of voluntary commitments and no enforceable accountability to show for it.
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