Build state AI evaluation on the ESSA evidence framework and integrate results into the existing education evidence base¶

Strategy · All strategies
Evidence · no claims cited

Description¶

In their discussion, the authors call on state leaders to co-design guidance with communities and to build collaborative approaches on the Every Student Succeeds Act (ESSA) of 2015, which established transparency expectations for educational outcomes. Integrating AI evaluation into the existing education evidence base is framed as creating an empirical foundation establishing what works and what does not for AI in learning environments, equipping leaders for informed decisions.

Design Implications¶

Context¶

Requirements¶

• Collaboration among state leaders, educators, researchers, and industry leaders
• Research-backed evidence evaluating AI in education

Constraints¶

Target Learners¶

• State education leaders
• District decision-makers

Target Learning Goals¶

• Evidence-based education policy and practice for AI-enabled technologies

Related Strategies¶

• Integrate co-design and continuous feedback loops into AI evaluation processes
• Establish AI-specific evaluation and accountability systems using independent audits, role-based dashboards, and privacy and equity safeguards
• 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
• Ensure communication and co-development with school leaders throughout edtech process rollout
• Provide synthesized and customized evidence tailored to the state context

Examples¶

-

Key Sources¶

• Ruiz, P., Prado, Y., Roschelle, J., & Fusco, J. (2025). What States Say About Evaluating AI in Education: Reviewing Guidance From 32 States and Puerto Rico. Digital Promise. https://doi.org/10.51388/20.500.12265/280