Pati Ruiz Awards RFP Overview Download the RFP as a PDF Frequently Asked Questions We are pleased to announce the awardees selected through the Teaching & Learning (T&L) Benchmarks and Datasets Request for Proposals. Each awardee is addressing a specific use case including: Adaptive Learning Experiences & Feedback for Students, Lesson & Instructional Planning for Teachers, … Read more
Authors: Pati Ruiz, Jordan Mroziak, Keun-woo Lee, Megan Pattenhouse In part 1 of this series, we discussed what an AI harnesses for education is, in part 2, we discussed why we need AI harnesses for education. In this final post of this 3-part series, we will dig deeper into the consideration for an AI harness … Read more
Authors: Pati Ruiz, Jordan Mroziak, Keun-woo Lee, Megan Pattenhouse In Part 1 of this series, we introduced AI harnesses as the technical layer that helps translate model capabilities into system behavior. For educational applications, this distinction matters because systems optimized for general-purpose assistance may not be optimized for learning. This post examines why harness-level mechanisms … Read more
Authors: Pati Ruiz, Jordan Mroziak, Keun-woo Lee, Megan Pattenhouse In an age where budgets are tightening and there is more work on an educator’s plate, there are clear areas of promise where Artificial Intelligence (AI) could automate processes and lighten educator’s loads. However, the inconsistency in the reliability, accuracy and privacy of AI models, as … Read more
From The Kapor Foundation by Shana V. White and Allison Scott, Ph.D. Download the Responsible AI and Tech Justice Guide for K-12 Education Amidst the debates on AI use in schools, its impacts on teaching and learning, and its potential for future job disruption, one fact remains clear: All students will need to be equipped … Read more
Suggested Citation: Kurimchak, M., Li, W., & Whitmer, J. (2026, July 3). Not ready yet AI infrastructure EdTech market research. OSF. https://doi.org/10.35542/osf.io/xjdnu_v3
Access the Scanner: Browse and filter K-12 AI resources including datasets, benchmarks, models, and competitions by grade level, academic subject, and source platform to evaluate and improve AI systems in education. Please share any resources you would like added to this database with k12ai-infrastructure [at] digitalpromise [dot] org
Authors: Rebecca Griffiths, Vanessa Peters Hinton, Shelton Daal, and Megan Pattenhouse https://doi.org/10.51388/20.500.12265/309 Abstract 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 … Read more
Authors: Matey Yordanov, Mikhail Bychkov, Andrei Kuchma Abstract: Traditional human marking in upper-secondary STEM education creates a structural bottleneck that restricts the frequency of formative mock examinations. This quasi-experimental, mixed-methods longitudinal study (N = 142) investigates the efficacy of deploying a fully automated, handwritten assessment marking platform to remove this bottleneck. Students preparing for STEM … Read more
Team: Dr. Hari Subramonyam, Mayank Sharma, and Dr. Roy Pea Overview: KB-TutorBench is a dataset and benchmark for training and evaluating AI tutors that support knowledge-building and formative assessment, using 6,000 annotated tutoring dialogues from simulated and real classroom interactions. Subject Focus: Earth Science, Chemistry, Biology, ELA, and History across grades K-5, 6-8, and 9-12. … Read more