ATS Hub Field Note: AI Model Evaluation

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Authors: Warren Li, Melanie Kurimchak, and John Whitmer https://doi.org/10.51388/20.500.12265/300 Abstract: The rapid integration of Artificial Intelligence into educational technology necessitates a rigorous approach to model evaluation, as the consequences of unchecked AI systems fall heavily on students, educators, and organizations. When AI tools are deployed to individualize learning, score student work, or generate feedback without … Read more

ATS Field Note: The UX Design Loop

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Authors: James Gartrell and Erik Johnson https://doi.org/10.51388/20.500.12265/299 Abstract: This field note introduces the 5-Phase user experience (UX) Design Loop as a transformative framework for education R&D teams. As AI technologies accelerate development cycles, this methodology helps teams navigate the shift from traditional static design to rapid, code-first prototyping. The loop emphasizes a cyclical, iterative process: … Read more

ATS Field Note: Using AI in Prototyping

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Authors: Cynthia McGurl, PhD and Bob Maisscotte https://doi.org/10.51388/20.500.12265/298 Abstract: Education Research and Development (R&D) demands efficient methods to bridge the gap between conceptualization and district-, school-, or classroom-ready solutions. This field note examines how AI-supported prototyping accelerates development, enabling R&D teams to transition from ideas to functional, testable tools. By integrating AI into structured workflows, … Read more

ATS Hub Field Note: Breakthrough Innovation: A Working Definition

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Authors: Kerry Friedman, Candice Benge-Steele, Adrienne Woods https://doi.org/10.51388/20.500.12265/297 Abstract: Breakthrough innovation in education requires more than incremental improvement, it demands fundamental shifts in how problems are understood, addressed, and measured. This field note presents a working definition of breakthrough innovation in education as a highly novel advance that redefines entrenched problems, enables significant improvements in … Read more

Enabling Uptake of AI Infrastructure in K12 EdTech Applications

Cover Enabling Uptake of AI Infrastructure in K12 EdTech Applications

Authors: Alexis Andres, John Whitmer, Melanie Kurimchak https://doi.org/10.35542/osf.io/vwn9c_v1 Abstract New investments in AI infrastructure aim to realize the potential of emerging technologies in K12 teaching and learning. To ensure these investments translate into usable applications, this report synthesizes open science policies from 41 institutions, including 15 philanthropies, 22 federal agencies, and 4 international organizations, to … Read more

Not Ready Yet AI Infrastructure EdTech Market Research

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Authors: Alexis Andres, John Whitmer, Melanie Kurimchak https://doi.org/10.35542/osf.io/ngbkv_v1 Abstract This study was conducted to ensure that emergent efforts to build an AI infrastructure for education technology are aligned with real-world needs. We conducted 15 interviews with 22 key stakeholders from Digital Learning Platform providers and R&D teams in the Learning Engineering Virtual Institute. We also … Read more

LEARN to Scale Toolkit

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The LEARN to Scale Toolkit is designed to support researchers and developers in creating, testing, and scaling evidence-based educational products that can achieve widespread and sustainable use and impact. The toolkit is organized into four key sections: Approach to Scaling introduces the LEARN Network’s systematic approach to scaling educational products using the Invent-Apply-Transition framework. Invent … Read more