Understanding the Artificial Intelligence Infrastructural Public Goods Needed for K-12 Education: Community Insights from a Request for Information

Keun-woo Lee, Jeremy Roschelle, Pati Ruiz
Proceedings of the Impactful and Responsible AI Systems for Education Workshop, PMLR 339:203-210, 2026.

Abstract

Learning at scale increasingly incorporates generative Al assets, with developers of products and services finding it useful to incorporate multiple Al components in their toolchain and to power particular features. Yet, the practices of choosing, tuning, and measuring Al components for inclusion in a broader product remains somewhat ad hoc, particularly with regard to evaluating Al according to learning sciences concepts. This has led researchers, industry, and funders to call for public goods that would be useful as infrastructure: datasets, benchmarks, and models that can measure learning sciences concepts, strengthen their operationalization, and provide guardrails to protect student privacy and safety. At the launch of a funding program related to this call, a public Request for Information (RFI) was launched and received over 100 responses from researchers, industry, and educators all over the world. Here we report on a work-in-progress to analyze the RFI responses. Using the responses, we answer questions that include: What kinds of infrastructural datasets, models and benchmarks are most needed to better include the learning sciences? What dimensions of learner variability should be emphasized? What privacy, safety and data governance issues are most in need of guardrails? This work presents community-informed insights on AI infrastructure in K-12 education as an initial roadmap to transition towards evidence-based public goods that serve all learners at scale.

Cite this Paper

BibTeX
@InProceedings{pmlr-v339-lee26b, title = {Understanding the Artificial Intelligence Infrastructural Public Goods Needed for K-12 Education: Community Insights from a Request for Information}, author = {Lee, Keun-woo and Roschelle, Jeremy and Ruiz, Pati}, booktitle = {Proceedings of the Impactful and Responsible AI Systems for Education Workshop}, pages = {203--210}, year = {2026}, editor = {Basu Mallick, Debshila and Woodhead, Simon and Wang, Zichao and Ananda, Muktha and Burstein, Jill and Murphy, April}, volume = {339}, series = {Proceedings of Machine Learning Research}, month = {28 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v339/main/assets/lee26b/lee26b.pdf}, url = {https://proceedings.mlr.press/v339/lee26b.html}, abstract = {Learning at scale increasingly incorporates generative Al assets, with developers of products and services finding it useful to incorporate multiple Al components in their toolchain and to power particular features. Yet, the practices of choosing, tuning, and measuring Al components for inclusion in a broader product remains somewhat ad hoc, particularly with regard to evaluating Al according to learning sciences concepts. This has led researchers, industry, and funders to call for public goods that would be useful as infrastructure: datasets, benchmarks, and models that can measure learning sciences concepts, strengthen their operationalization, and provide guardrails to protect student privacy and safety. At the launch of a funding program related to this call, a public Request for Information (RFI) was launched and received over 100 responses from researchers, industry, and educators all over the world. Here we report on a work-in-progress to analyze the RFI responses. Using the responses, we answer questions that include: What kinds of infrastructural datasets, models and benchmarks are most needed to better include the learning sciences? What dimensions of learner variability should be emphasized? What privacy, safety and data governance issues are most in need of guardrails? This work presents community-informed insights on AI infrastructure in K-12 education as an initial roadmap to transition towards evidence-based public goods that serve all learners at scale.} }
Endnote
%0 Conference Paper %T Understanding the Artificial Intelligence Infrastructural Public Goods Needed for K-12 Education: Community Insights from a Request for Information %A Keun-woo Lee %A Jeremy Roschelle %A Pati Ruiz %B Proceedings of the Impactful and Responsible AI Systems for Education Workshop %C Proceedings of Machine Learning Research %D 2026 %E Debshila Basu Mallick %E Simon Woodhead %E Zichao Wang %E Muktha Ananda %E Jill Burstein %E April Murphy %F pmlr-v339-lee26b %I PMLR %P 203--210 %U https://proceedings.mlr.press/v339/lee26b.html %V 339 %X Learning at scale increasingly incorporates generative Al assets, with developers of products and services finding it useful to incorporate multiple Al components in their toolchain and to power particular features. Yet, the practices of choosing, tuning, and measuring Al components for inclusion in a broader product remains somewhat ad hoc, particularly with regard to evaluating Al according to learning sciences concepts. This has led researchers, industry, and funders to call for public goods that would be useful as infrastructure: datasets, benchmarks, and models that can measure learning sciences concepts, strengthen their operationalization, and provide guardrails to protect student privacy and safety. At the launch of a funding program related to this call, a public Request for Information (RFI) was launched and received over 100 responses from researchers, industry, and educators all over the world. Here we report on a work-in-progress to analyze the RFI responses. Using the responses, we answer questions that include: What kinds of infrastructural datasets, models and benchmarks are most needed to better include the learning sciences? What dimensions of learner variability should be emphasized? What privacy, safety and data governance issues are most in need of guardrails? This work presents community-informed insights on AI infrastructure in K-12 education as an initial roadmap to transition towards evidence-based public goods that serve all learners at scale.
APA
Lee, K., Roschelle, J. & Ruiz, P.. (2026). Understanding the Artificial Intelligence Infrastructural Public Goods Needed for K-12 Education: Community Insights from a Request for Information. Proceedings of the Impactful and Responsible AI Systems for Education Workshop, in Proceedings of Machine Learning Research 339:203-210 Available from https://proceedings.mlr.press/v339/lee26b.html.

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