Concentrate AI-edtech adoption priorities through collaborative networks that share tools, policies, and evaluation practices¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The brief recommends that state and district leaders scale and sustain AI-enabled edtech adoption through formal or semi-formal partnerships across educational jurisdictions. It argues that "Establishing and leveraging collaborative networks that concentrate efforts on strategic priorities, rather than pursuing disparate initiatives, can help educational leaders accelerate responsible AI adoption and maximize limited resources." Examples include Washington State's human-centered AI guidance development, Utah's student data privacy office, and intermediaries like SETDA and CoSN.

Design Implications¶

Context¶

Requirements¶

• Representatives from different educational jurisdictions working together as a network
• Systematic investment in capacity-building infrastructure including regular audits and comprehensive professional learning

Constraints¶

Target Learners¶

• PK16 students in state, district, and local education systems

Target Learning Goals¶

• Responsible adoption of AI-enabled educational technologies
• Preparation for an AI-influenced workforce

Related Strategies¶

• Foster communities of practice for AI governance instead of rigid top-down policies
• Districts should build community-shared visions, evaluation infrastructure, and policies for procuring and using AI edtech
• Establish AI-specific evaluation and accountability systems using independent audits, role-based dashboards, and privacy and equity safeguards
• Four strategic recommendations for supporting AI literacy across settings
• Develop data-sharing memoranda of understanding and shared evaluation across child- and family-serving agencies
• Use shared frameworks, cross-district collaboration, and state-level policy alignment to scale AI-enabled edtech
• Sustain educator learning through schoolwide, network, and districtwide approaches that embed professional learning across the system
• Educators share experiences, strategies, and insights about generative AI tools to keep the education community informed
• Combine bottom-up co-design with top-down executive sponsorship, cross-unit collaboration and dedicated R&D capacity to embed institutional GenAI

Examples¶

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Key Sources¶

• Ruiz, P., Peters Hinton, V., Prado, Y., Noakes, S., Liberman, B., Chillmon, C., Van Nostrand, P., Shell, A., Karim, S., Dallas, D., & Zachariah, I. (2025). Integrating AI-enabled edtech in PK16 education: Five recommendations for state and district leaders. Digital Promise. https://doi.org/10.51388/20.500.12265/273