Pati Ruiz 
Author: K-12 AI Infrastructure Program Team 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 well as the … 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
Team: Allison Koenecke, Rachel Slama, Rene Kizilcec, and Kirk Vanacore Overview: This project addresses a key K–12 AI infrastructure gap: ASR systems underperform for children and marginalized speakers. We will expand tutoring audio datasets, release a child-optimized ASR baseline, and create a public leaderboard to benchmark models using education-specific metrics. Subject Focus: K-12 math tutoring … Read more
Team: Tammy Kwan and Brenden Lake Overview: This project develops a training pipeline for neural-network-based simulated student models. The approach combines synthetic reasoning traces generated by rule-based student models with human student reasoning data to substantially reduce the amount of human data required. Subject Focus: Fraction arithmetic within grades 6-8, with exploratory transfer to decimals, … Read more
Team: Richard Tibbles, Jamie Alexandre Overview: Science misconception research spans decades but isn’t operationalized for classroom use. Three public goods, all openly licensed: a benchmark for identifying science misconceptions in free-form student responses, a structured dataset, and reference classifiers for classroom devices. Subject Focus: Grades 6-12 Earth, Life, and Physical sciences Targeted Universalism Focus: The … Read more