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Peters Hinton, Shelton Daal, Megan Pattenhouse August 2026  \nTable of Contents  \nTable of Contents...................................................................................................1  \nExecutive Summary............................................................................................... 2  \nI. Context......................................................................................................... 4  \nII. Data Sources................................................................................................ 6  \nIII. Findings...................................................................................................... 7  \n3.1. What Educators Want AI to Do....................................................................... 7  \n3.1.1. Ground AI in District Values, Curricula, and Student Context.......... 7  \n3.1.2. Increase Capacity to Provide Formative Assessment.....................8  \n3.1.3 Better Serve Multilingual Learners, Students with Disabilities, and Rural Communities.....................................................................................9  \n3.1.4 Return Time and Capacity to Overburdened Staff.......................... 11  \n3.2. Where Current AI Tools Fall Short................................................................12  \n3.2.1. Generic Tools Undermine Curriculum Coherence and Pedagogical Goals.........................................................................................................12  \n3.2.2. Student-facing AI Raises Serious Wellbeing, Equity, and Safety Concerns.................................................................................................. 12  \n3.2.3. Proxies for Trust Do Not Address Underlying Safety and Fairness Concerns.................................................................................................. 14  \n3.2.4. Evidence and Evaluation Standards Are Immature....................... 15  \n3.3. How Developers Are Responding................................................................ 16  \n3.3.1. Adapting Frontier Models for Educational Use Cases................... 16  \n3.3.2 Optimizing Model Selection and Implementation...........................17  \n3.4 What Infrastructure Is Needed......................................................................17  \n3.4.1. Knowledge Graphs and Ambient Assessment...............................18  \n3.4.2. Child Speech Recognition and Representative Voice Datasets... 18  \n3.4.3. Longitudinal and Holistic Student Data Infrastructure.................. 19  \n3.4.4. Rigorous, Shared Evaluation Benchmarks................................... 20  \n3.4.5. Context Engineering Infrastructure.............................................. 20  \nIV. Conclusions............................................................................................... 21  \nReferences......................................................................................................... 24  \nMarket Landscape Interview Participants..................................................................... 27  \nRecommended Citation  \nGriffiths, R., Peters Hinton, V., Daal, S., Pattenhouse, M. 􀖤 2026, July) . K􀖞12 AI Infrastructure: Findings from educator and developer outreach. Digital Promise.  \n[https://doi.org/10.51388/20.500.12265/309](https://doi.org/10.51388/20.500.12265/309)  \nDigital Promise® K􀖽12 AI Infrastructure: Findings from Educator and Developer Outreach 1  \nExecutive Summary  \nThe K􀖽12 AI Infrastructure Program funds public goods such as datasets, models, benchmarks and other modular, shareable components needed to make AI systems work better for educational purposes. Over the past year, we have solicited input from educators, edtech leaders, researchers, and other stakeholders to inform our investment strategy. We share our findings here, with particular attention to identifying types ","cbCaikFsI7Co2lF2","https://ap.wps.com/l/cbCaikFsI7Co2lF2","pdf",2461847,"English","# Executive Summary\n# Context\n# Data Sources\n# Findings\n## What Educators Want AI to Do\n## Where Current AI Tools Fall Short\n## How Developers Are Responding\n## What Infrastructure Is Needed\n# Conclusions\n# References","[{\"question\":\"What is the purpose of the K-12 AI Infrastructure Program?\",\"answer\":\"It funds public goods such as datasets, models, benchmarks, and other modular components to make educational AI systems work better.\"},{\"question\":\"What do educators want AI tools to do?\",\"answer\":\"Educators want AI applications that embed local context, curricula, and student needs, rather than relying on generic chatbots that may undermine instructional goals.\"},{\"question\":\"Which infrastructure components are identified as needed for the next phase?\",\"answer\":\"The report calls for knowledge graphs and ambient assessment, child speech recognition and representative voice datasets, longitudinal holistic student data infrastructure, rigorous shared evaluation benchmarks, and context engineering infrastructure.\"}]","K-12 AI Infrastructure: Findings from Educator and Developer Outreach | PDF",1792050848,76]