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¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The report synthesizes the state documents' advice into a common roadmap for local education leaders. The steps are to engage with communities, apply existing policies and develop additional AI policies aligned with regulations such as FERPA, COPPA, IDEA, and CIPA, provide professional development for educators, create a supportive learning environment with equitable device and internet access, and monitor and evaluate AI use. As the report states, "The roadmap below reflects common steps that appear across many of the documents."

Design Implications¶

Context¶

Requirements¶

• Policies must be aligned with regulations such as FERPA, COPPA, IDEA, and CIPA to ensure compliance with data privacy and security laws
• Acceptable Use and other policies should be regularly reviewed and updated as needed

Constraints¶

• The roadmap reflects steps common across many, not all, of the seven state documents

Target Learners¶

• K-12 students

Target Learning Goals¶

• Responsible and effective integration of AI into teaching and learning

Related Strategies¶

• Districts should build community-shared visions, evaluation infrastructure, and policies for procuring and using AI edtech
• Foster communities of practice for AI governance instead of rigid top-down policies
• Mobilize & Implement: provide professional learning throughout the pilot and use real-time data dashboards to keep usage aligned with dosage requirements
• Design & Plan: build a professional learning and measurement plan to monitor implementation fidelity and evaluate impact
• Co-create contextualized AI literacy policies via multi-stakeholder task forces with phased roadmaps
• Build state AI evaluation on the ESSA evidence framework and integrate results into the existing education evidence base
• Establish comprehensive institutional AI governance frameworks including task forces, roadmaps, and transparent policies
• Provide synthesized and customized evidence tailored to the state context
• Legislators should pass consumer-protection legislation and create a regulatory agency governing AI education products
• Provide professional development on the pedagogy and content of ethnic studies before implementing new ethnic studies standards
• Adopt AI incrementally in waves from simple to complex tasks, on free-tier tools, with mandatory multi-step human review before any document is issued
• Use the taxonomy as a self-assessment and strategic-planning roadmap for institutional AI adoption

Examples¶

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Key Sources¶

• Roschelle, J., Fusco, J. & Ruiz, P. (2024). Review of Guidance from Seven States on AI in Education. Digital Promise. https://doi.org/10.51388/20.500.12265/204