Knowledge Hub

Developed by Digital Promise in partnership with SAP and the Learner-Centered Collaborative, this design guide serves as a practical toolkit for K-12 school and district leaders seeking to launch equity-focused alumni surveys. The guide details a collaborative co-design approach piloted and refined with multiple school and district partners between 2021 and 2025. Utilizing this Alumni Survey model leverages the lived experiences of students, staff, families, and graduates shifting educational research from designing for communities to designing with them. Central to the resource is a comprehensive nine-step technical roadmap that navigates teams through the survey design and launch so that their efforts translate into genuine institutional change. Real-world impact stories from partner districts illustrate how they have used their alumni data to transform their programs, address educational equity, and improve student outcomes. This guide concludes with strategic recommendations for educational leaders and researchers interested in maximizing the value of alumni feedback.

This report presents findings from the K-12 AI Infrastructure Program's outreach to educators, edtech developers, and other stakeholders, conducted during late 2025 to mid-2026. It examines what educators want from AI tools, where current AI applications fall short, how education technology developers are responding, and what public infrastructure is needed to bridge these gaps. Educators want AI systems grounded in their district's curricula, values, and student populations, capable of supporting formative assessment at scale, and able to return time to overburdened staff. However, generic AI tools can undermine curriculum coherence, and student-facing applications raise concerns about effectiveness, wellbeing, and safety. Existing trust proxies, such as certifications, fail to address underlying privacy and fairness issues, while evaluation standards for AI-based edtech remain immature. The report identifies five priority infrastructure gaps: knowledge graphs supporting ambient assessment, child speech recognition and representative voice datasets, longitudinal and holistic student data systems, shared evaluation benchmarks, and context engineering infrastructure. The authors argue that publicly available, modular infrastructure—rather than proprietary, vendor-specific solutions—is needed to help AI tools better align with pedagogical goals while protecting student privacy and serving historically underserved populations.