NSF 26-501: Collaboratory to Advance Mathematics Education and Learning for K-12 (CAMEL)

NSF 26-501: Collaboratory to Advance Mathematics Education and Learning for K-12 (CAMEL)

The NSF 26-501 solicitation is a high-quality open funding opportunity for institutions and research networks working on mathematics learning, especially where data science and educational practice can be connected. It is one of the better structured federal education grants because it defines a sequence: Phase I builds one or more collaborative networks that produce high-value datasets or improve existing datasets, and Phase II extends successful Phase I projects into a shared national collaboratory with coordinated outcomes assessment. That structure makes it both a research grant and an ecosystem program.

If you are trying to choose between broad education grants, this one is different from typical short-term pilot funding because it is explicitly designed to generate reusable community assets (datasets, tools, protocols, training pathways) and to keep those assets connected through collaboration infrastructure after the project start. For teams with strong partnerships between education researchers and classroom practitioners, it can also be a strong path to scale.

Key details at a glance

Why this opportunity is a good match for serious applicants

CAMEL is not simply a “produce a tool” grant. Its funding logic expects evidence-based collaboration and measurable outputs in teaching, learning, and data generation. The official solicitation states that CAMEL is intended to connect multiple disciplines: science of learning, cognitive and social science, computer science, machine learning, engineering, and education research, while also pulling in educational practice. That means proposals are rewarded when they treat mathematics education as an ecosystem problem rather than a one-off research intervention.

The opportunity is particularly useful for teams that can do both research and implementation-oriented work. NSF calls out the need for collaborative networks that combine theory and practice. A grant team that includes data scientists with practical classroom links and a team of researchers who can tie interventions to outcomes is generally more competitive than a grant that only proposes models or only does pilot studies. The call is explicit about producing high-value datasets that are usable by the broader field and likely to generate cumulative value.

The same structure also helps with long-term impact. Phase I is where networks and datasets are formed; Phase II is where outcomes are institutionalized through a national collaborators model, with a “collaboratory” built for continued coordination and training. Even if your main ask is to run one project, you should design as if the work can outlive the initial grant period.

What exactly Phase I and Phase II mean in practice

Many teams misread Phase I and Phase II as separate programs with independent applicant pools. In this solicitation, Phase II is only open to Phase I awardees. That has two consequences:

1. Every successful Phase I application should be built with a plausible handoff plan to a Phase II collaborative model.
2. Institutions applying only for Phase II are out; they must secure Phase I outcomes first.

Phase I itself has two pathways:

• 1a Novel Dataset Pathway: create new high-value K-12 math datasets where current collections are insufficient.
• 1b Existing Dataset Pathway: repurpose and increase value of existing K-12 math datasets with support for curation and safe sharing.

Both pathways are judged on quality of the research-to-practice loop and on technical rigor in data handling. The solicitation lists concrete expectations for each dataset: representativeness of learners, privacy controls, AI-readiness, size/quality sufficient for analysis, and FAIR principles (Findable, Accessible, Interoperable, Reusable). If your team cannot explain how data is collected, governed, annotated, and reused beyond your own project boundary, this is a strong reason to defer.

The Phase II concept is intentionally different: it is not just a “Phase I extension.” NSF describes Phase II as a collaboratory with a national coordinator, collaborative infrastructure, training in data science, and outcomes tracking. A project that gets into Phase II should already show that it can act as part of a connected network with shared standards and reproducible practice.

Eligibility and proposal constraints you must design around early

The eligibility section is broad in organizational type but strict in role and submission structure.

For proposal submission, the solicitation allows:

• Two- and four-year U.S. institutions of higher education (including community colleges), acting on behalf of their faculty.
• Non-profit, non-academic U.S. educational/research organizations.
• Federally recognized American Indian and Alaska Native tribal entities.

This combination means many organizations that do not hold university status can participate, but proposal ownership still follows NSF submission rules. Programmatic collaboration with museums, labs, or similar organizations can be strong if they are aligned with educational and research activity.

Other constraints with practical impact:

• LOI is mandatory for Phase I.
• PI or co-PI may submit only one Phase I proposal.
• Partners with Walton Family Foundation staff cannot be included as collaborators in proposals.
• Cost sharing is specifically prohibited.
• Collaborative proposals are restricted in system logistics; multiple organization submissions must go through Research.gov.

The PI limit is often underestimated. Because the solicitation permits only one Phase I PI/co-PI per applicant identity, institutions should centralize role planning before concept notes are drafted. Running parallel internal projects in the same institution can run afoul of this if roles are reused without governance clarity.

Application timing and what the 2026/2027 window really implies

This is one of the few opportunities where the timeline gives you an obvious near-term anchor and a visible continuity path. For 2026, Phase I has concrete deadlines: LOI on 2026-01-09 and full proposals on 2026-03-11. Phase II is accepted anytime, but only for Phase I awardees.

For teams looking at 2027 planning, this means two possible strategies:

• Strategy A (fast track to visibility in 2026): submit a high-quality Phase I package by the March 11 deadline and build a credible transition to Phase II.
• Strategy B (phased preparation): use the current phase structure to complete pilot governance, partner agreements, and data planning now, then prioritize a polished Phase I package when needed.

A frequent mistake is treating “ongoing” Phase II as a second independent window. Since it depends on Phase I award status, the real gating mechanism is whether your Phase I submission and results can support expansion. So your internal timeline should treat Phase II as a continuation that starts before Phase I ends, not after with a blank reset.

Required materials and proposal architecture that actually improve odds

The solicitation requires a required LOI, not optional. It also defines a specific naming convention and technical materials expected from teams.

Core submission requirements

• LOI via Research.gov by 2026-01-09.

• Full proposal by 2026-03-11 (Phase I).

• Proposal title conventions:

  • Phase I titles should begin with “Phase I CAMEL-CN”
  • Phase II titles should begin with “Phase II CAMEL”

• One-page synopsis requirements for LOI:

  • math learning and data science goals
  • network composition and interdisciplinary value
  • dataset description and justification
  • anticipated high-impact outcomes
  • training needs/challenges addressed
  • up to five comma-separated keywords, including learner age range, math area, and data format
  • pathway designation (Phase 1a, 1b, or both)

• Project description must address collaboration mechanisms, resource quality, alignment to standards, use cases, implementation plan, evaluation, and management approach.

• Collaboration and Management Plan (up to 2 pages) is required.

When teams over-prepare the science without showing collaboration mechanics, they often get low marks. This program is unusually explicit that collaboration is not optional. NSF is interested in how researchers and K-12 practitioners will co-design and co-produce knowledge over time.

Budget and compliance details that can quietly hurt applicants

The funding level is substantial per award, but not huge compared with broad national programs, so budget structure matters. The solicitation caps likely award size at $1.5M over up to three years and sets no voluntary cost-sharing.

Key points:

• no committed cost sharing required, and voluntary committed cost sharing is prohibited,
• standard NSF cost principles apply,
• data generation and sharing plans are central, not peripheral.

The most common budget issue in this kind of program is weak linkage between spending and collaborative outputs. NSF reviewers expect expenditure logic that supports sustained data curation, training use cases, community coordination, and evaluation. If costs are only tied to pilot content, proposals look unsustainable.

Who this is strongest for (and who should likely skip)

This is strongest for teams that can demonstrate these capabilities:

• proven collaboration across disciplines,
• access to K-12 practice settings or practitioner collaborators,
• clear plans for quality and reusable datasets,
• team members with experience in research methods plus implementation workflows,
• long-horizon planning for network building and training capacity.

Examples of strong fits include:

• university education or data science groups with established school partnerships,
• education labs with experience in machine learning for learning diagnostics,
• interdisciplinary consortia involving school districts, researchers, and technical specialists,
• institutions that can handle data governance and privacy for learner-linked resources.

If your current team is primarily theoretical and lacks either school-level practitioner ties or a data engineering backbone, consider applying only after building at least one pilot data governance framework and a shared evaluation protocol. The program is less suited for one-off classroom interventions without data reuse design.

Review expectations and common mistakes

NSF uses standard merit criteria of Intellectual Merit and Broader Impacts, with program-specific filters layered on top. In CAMEL, successful proposals are usually the ones that can show:

• strong scientific and educational relevance,
• clear use-case path from data to outcomes,
• practical plan for collaboration and coordination,
• explicit handling of privacy and FAIR principles,
• credible plan for measuring impact (not just output metrics).

Common weaknesses that can weaken an application:

• treating the dataset as an internal product only,
• weak or generic collaboration plans,
• missing the required pathway clarity (1a vs 1b),
• insufficient justification for why a dataset is “high value,”
• ignoring PI limit constraints and filing multiple PI submissions,
• not documenting how outputs will transition into collaboration infrastructure.

A strong application usually reads like a design document, not just a grant proposal. It should explain not only what you will do, but how your team will ensure other organizations can actually use what you produce.

Practical preparation checklist before the LOI

Use this pre-submission order and keep a shared tracker:

1. Confirm organizational eligibility.
2. Assign one official PI and ensure no PI/co-PI duplicates an additional Phase I proposal.
3. Choose pathway(s): 1a and/or 1b, and test each against available datasets.
4. Draft one-page LOI with explicit outcomes and data value statement.
5. Prepare a 1-page keywords line (age ranges, math domains, data format).
6. Draft a collaboration and management section with named roles: principal investigators, practitioner liaisons, data governance lead, evaluation owner.
7. Confirm Research.gov account access and submission owner if using external collaborators.
8. Draft privacy and FAIR plan: de-identification strategy, metadata standards, sharing mechanism.
9. Build a review timeline backward from 2026-01-09 LOI and 2026-03-11 full proposal.

If you start after the year starts, this still works, but teams that do this in time generally submit more coherent docs because they can align the one-page LOI and full proposal requirements simultaneously rather than retrofitting later.

FAQ

Is this still open now?

Yes. The solicitation is marked as an active funding opportunity with a posted date in 2025 and current open instructions for 2026 Phase I and indefinite Phase II intake for awardees.

Can my institution submit both pathways?

Yes, proposals may be submitted through 1a and/or 1b, but the submission title must follow the required format and you must clearly justify each track and its distinct outcomes.

Can applicants without a traditional education department participate?

Yes, if they meet eligibility as an allowed organization type and are proposing a team with clearly relevant expertise. Many strong applications are interdisciplinary and do not come from a single education department.

Is there any funding year guarantee?

The amount listed is anticipated and contingent on available funds and competitive proposals. Expected FY2026 available amount is up to $9.0M, but NSF does not guarantee funding levels.

Do you need letters of intent?

For Phase I, the LOI is required and must be submitted before the full proposal.

Risks, limitations, and next steps for applicants

The biggest risk is treating CAMEL as just another dataset grant. It is designed as a collaborative network and ecosystem project. If your logic ends at data collection and publication, reviewers may view it as incomplete.

Second risk is submission mechanics. The required one-title convention, LOI timing, PI restrictions, and Research.gov requirements for collaborative submissions are straightforward but can create automatic disqualifications if missed.

Third risk is underestimating the transition from Phase I to Phase II. Even if Phase II has no fixed date, proposal quality should already include continuity planning.

Because this is a federal program with institutional-level governance, teams should assume a minimum of several internal approvals, legal/data governance checks, and review cycles before submission. Build that process as a project component from the start.

Official links

• Official solicitation (official source): https://www.nsf.gov/funding/opportunities/collaboratory-advance-mathematics-education-learning-k-12/nsf26-501/solicitation
• NSF program page: https://www.nsf.gov/funding/opportunities/collaboratory-advance-mathematics-education-learning-k-12
• NSF PAPPG page (proposal rules): https://www.nsf.gov/policies/pappg_23_1/nsf36/
• NSF Proposal & Award process: https://www.nsf.gov/awardsearch

Before submitting, keep your working notes focused on evidence and compliance, and avoid writing to fill pages. CAMEL rewards proposals that can be read as credible, data-responsible, and durable for K-12 learning ecosystems.