Use shared frameworks, cross-district collaboration, and state-level policy alignment to scale AI-enabled edtech¶

Strategy · All strategies
Evidence · no claims cited

Description¶

For the scaling and sustaining phase, the article recommends developing standardized frameworks, strengthening state-level policy coordination, and coordinated funding structures. It states these would allow districts to "leverage shared resources and collaborative approaches, reducing the current need for each district to independently build complex procurement systems."

Design Implications¶

Context¶

Requirements¶

• Coordinated funding structures
• Policy alignment at state and local levels

Constraints¶

• Key challenges named are funding constraints, capacity limitations, and market fragmentation

Target Learners¶

• District and state education leaders

Target Learning Goals¶

• Equitable access to AI-enabled edtech across student populations

Related Strategies¶

• Concentrate AI-edtech adoption priorities through collaborative networks that share tools, policies, and evaluation practices
• Establish AI-specific evaluation and accountability systems using independent audits, role-based dashboards, and privacy and equity safeguards
• House the certifying entity within structures and policies that promote alignment, buy-in, and sustained funding
• Collaborative cross-cultural research agenda for validating AI-enabled pedagogical accompaniment

Examples¶

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Key Sources¶

• Ruiz, P., and Noakes, S. (2025). AI-Enabled Edtech Adoption Cycle. Digital Promise. https://doi.org/10.51388/20.500.12265/272