Districts should build community-shared visions, evaluation infrastructure, and policies for procuring and using AI edtech¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The report recommends that districts collaborate with community members to develop a shared vision, then determine "where might AI be useful and where might it be harmful." It argues districts hold "immense, yet underutilized, power in this market": when districts uphold policies responsive to AI's risks, developers are incentivized toward safer, more ethical solutions. It also calls for investment in technical infrastructure for evaluation and decision making, and for rapid, rigorous efficacy research to keep pace with AI's rate of change.

Design Implications¶

Context¶

Requirements¶

• District collaboration with community members to articulate local priorities and needs
• Access to industry benchmarks or standards for ethically designed AI technologies

Constraints¶

• Only four submissions prioritized district infrastructure and policy development, which the report calls an unmet need

Target Learners¶

• District and school leaders
• PK-12 students and teachers

Target Learning Goals¶

• Ethical procurement and effective use of AI-enabled edtech
• Equitable outcomes in mathematics education

Related Strategies¶

• Structure OBC procurement around intentionality questions on learner goals, populations, outcomes, and stakeholders
• Support future edtech OBC cohorts with readiness assessments, early planning, needs-assessment framing, and cross-cohort collaboration
• Foster communities of practice for AI governance instead of rigid top-down policies
• District actions for sustained, equitable impact with powerful technology
• School districts adopting AI should follow a five-step roadmap: engage communities, apply and develop policies, provide professional development, create supportive learning environments, and monitor and evaluate AI use
• System-level strategies to build enabling conditions for Powerful Learning
• Co-create contextualized AI literacy policies via multi-stakeholder task forces with phased roadmaps
• Concentrate AI-edtech adoption priorities through collaborative networks that share tools, policies, and evaluation practices
• Convene cross-functional generative AI task forces to guide ethical adoption decisions and procurement
• Four strategic recommendations for supporting AI literacy across settings
• Prioritize evidence-based technology programs and implement them with professional learning and integrated practice
• Use generative AI to make assessment systems more instructionally relevant, not to reify problematic practices
• Use the taxonomy as a self-assessment and strategic-planning roadmap for institutional AI adoption
• Cross-stakeholder policy, research, and practice actions for collaborative AI initiatives in education
• Structure opportunities for students to evaluate AI outputs using locally grounded criteria of relevance, harm, and usefulness
• Prioritize construct-to-data mappings, multimodal validation, ethical implementation, and cross-disciplinary collaboration for domain-specific adaptive systems

Examples¶

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Key Sources¶

• Noakes, S., Shell, A., Van Nostrand, P., Murillo, A., Ruiz, P., and Liberman, B. (2024, September). AI-Powered Innovations in Mathematics Teaching & Learning: Initial Findings. Digital Promise. https://doi.org/10.51388/20.500.12265/229