AIMS EduData Phase II RFP - Apply by December 14, 2026

Applications due December 14, 2026

The AIMS EduData initiative invites researchers, educators, and digital learning platform (DLP) developers to propose research and development projects to pursue insights about mathematics teaching and learning through one of three approaches.

Leveraging Benchmark Data Collection

This opportunity invites researchers and educators to use deidentified data from the University of Maryland Center for Educational Data Science & Innovation (EDSI)’s Benchmark Data Collection to study mathematics teaching and learning in grades 4–8.

Projects may generate insights into student learning, motivation, participation, and classroom practices; identify and measure teaching practices that contribute to student learning; develop and validate computational methods and measures; or develop and evaluate tools and resources for teachers’ professional learning.

The Benchmark Data Collection includes deidentified classroom transcripts and linked student and teacher surveys, demographic, and achievement data. Research areas of particular interest include:

• Student mathematical reasoning and how teachers elicit and respond to students’ ideas.

• Participation, engagement, and opportunities to contribute to classroom discussions.

• Relationships between instructional practices, reported classroom experiences, and learning outcomes.

• Development and validation of computational measures, including their reliability and limitations.

• Development and evaluation of tools and resources that support teachers’ professional learning.

We especially encourage applications from early career researchers and educators, including tenure-track faculty, postdoctoral researchers, graduate students, researchers within nonprofit organizations, district staff with research roles, and researchers at minority-serving institutions or Title I schools.

Funding: Four grants of up to $50,000 each for 12 months, with the opportunity to extend.

Earliest start date: February 1, 2027

Integration of Carnegie Learning’s UpGrade Tool

This opportunity invites early and mid-stage digital learning platforms focused on K–12 math to propose projects that involve integrating Carnegie Learning’s UpGrade tool to support research on their platforms.

UpGrade is an open-source tool for conducting A/B tests and other types of experimental designs in EdTech platforms. It was developed and is managed by Carnegie Learning, which will provide technical support during platform integration.

Successful applicants will propose a software integration that involves:

• Integrating UpGrade into an existing digital learning platform.

• Conducting a demonstration A/B test or other experimental design on their platform using UpGrade.

Strong candidates will have established platforms that are instrumented to collect data reflecting learning or motivational outcomes, such as correctness, time on task, hint use, and self-efficacy, and/or support teachers in using their time toward more effective practices.

Both commercial and nonprofit organizations are invited to apply. Platforms may have previously leveraged A/B testing in a small capacity but have not been involved in running multiple randomized trials.

Funded platforms will also participate in a second phase of RFPs in 2027 to support short-term research partnerships using UpGrade. Research priorities include supporting students’ motivation, engagement, and persistence in K–12 math and helping teachers save time or use time more effectively.

Funding: A $60,000 grant for up to 12 months.

Earliest start date: January 1, 2027

Secondary Research with ASSISTments Data through SafeInsights

This opportunity invites researchers to propose secondary research using existing data from ASSISTments, an online math platform. Researchers will utilize the data through SafeInsights.

SafeInsights is an education research hub that connects researchers, data from educational apps, and schools to understand how students learn across contexts and over time, while protecting student privacy. Researchers write analysis code that runs where the data lives, and only aggregate results that pass a privacy review are returned to them.

ASSISTments is an online math platform that supports teachers and students in grades 3–10. Teachers assign problems from widely used curricula, and students receive immediate feedback, hints, explanations, and multiple attempts as they work.

ASSISTments is particularly interested in research that advances our understanding of:

• How students learn mathematics.

• How learning and progress can be measured.

• How teachers assign mathematics practice and formative assessment in ASSISTments.

We favor proposals that use small, local, and efficient models, produce generalizable findings, and offer clear pedagogical implications.

Funded researchers will be among the first to conduct research through SafeInsights. Beyond their findings, the initiative also seeks to learn what it takes to conduct research in a secure enclave using a compute-to-data approach.

We especially welcome early career researchers, including graduate students and postdocs.

Funding: Two grants of up to $50,000 each for up to six months.

Earliest start date: February 1, 2027.

Application Information

In addition to funding, grantees will be involved in a community of practice and engage with digital learning platform and research staff, fellow grantees, and the broader AIMS Collaboratory network.

All research will require completion of ethical and legal data usage requirements, including IRB applications, privacy requirements, and applicable data use policies and procedures.

Application deadline: December 14, 2026

View the full RFP and application details

For questions, contact aimsedudata@digitalpromise.org