A primary theme of the report is the shift to evidence-based decision-making through the use of co-design and feedback loops. We believe educators should not be passive users of AI; instead, incorporating their voices with those of students and parents, is essential in implementing continuous improvement cycles. Many states, such as Alabama and Massachusetts, have established dedicated working groups who ensure that AI implementation is multi-directional and transformative rather than simply a top-down mandate. This approach is designed to ensure that AI tools are equitable, accessible, and centered on human agency and the needs of districts and schools.
As AI technologies are constantly evolving, the report encourages educators to engage in a collaborative culture of feedback and reflection, helping to build a foundational framework for an evidence base that identifies what truly works for learners in real-world learning settings.
Our analysis suggests that many states are starting to become more structured in their evaluation of AI, moving beyond simple satisfaction surveys to tracking specific qualitative and quantitative metrics. For example, Wisconsin is already piloting AI in specific subjects, such as math, and comparing student progress against control classes to measure impact. Other states, like Oklahoma, are requiring that vendor assessments include evidence of effectiveness and impact.
Educational leaders need to create mechanisms for regular feedback from learners and teachers, incident reporting, and tracking of educational outcomes across different demographics to ensure responsible use.
Finally, while state guidance on AI in education is advancing, we identified a critical need for states to move toward systematic, evidence-based implementation. A few states, such as Colorado and Louisiana, are tracking student progress and using metrics on AI access and engagement to evaluate educational outcomes from the use of AI tools.
Education leaders are encouraged to progress from “Nascent” stages to “Systematic and Evidentiary” frameworks by embedding qualitative and quantitative metrics into their local contexts. This can be done through partnerships with researchers, non-profits, and member-serving organizations. By establishing clear standards for transparency and impact, leaders can build a foundation for AI that truly enhances the educational experience.
The long-term impact of AI in education will hinge on how states balance advances in AI with clearly defined educational goals and research, including rigorous evaluation and clear reporting. Moreover, traditional evaluation approaches may no longer be sufficient to ensure the learning efficacy and safety of rapidly evolving learning technologies. Educators, researchers, learners, and community members all have a role to play in evaluating how AI is integrated into learning environments.
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