LEARNING AGENCY LAB

As filed

Subsection

03

NTEE code

B90

Classification

2000

Street

2307 S RURAL RD

These codes are the IRS's own and are shown exactly as filed. What each one means is in the Business Master File documentation (opens in a new tab).

Other organizations at this address

More than 50 organizations in the IRS Business Master File share this street address, so they are not listed here. matched by us

We match the street line and ZIP code as the IRS gives them, ignoring case and outer spaces and nothing else, so "AVE" and "AVENUE" count as different streets and this list can miss some. A shared address can mean related organizations or a shared office, but it can also be a registered agent, a mail drop or a service provider holding mail for unrelated clients. It is not, by itself, evidence that two organizations are connected.

Mission and programs · 2024

• In the 2024 fiscal year, The Learning Agency Lab further expanded its efforts to advance educational technology through data-driven research, collaborative competitions, and open-access resources. Over the course of the year, the Lab completed four major competitions in the Open Data Assessment Fund (ODAF) series, each designed to develop state-of-the-art algorithmic solutions that improve teaching, assessment, and data privacy in education. These competitions not only engaged a global community of data scientists and researchers but also generated open-source tools that can be applied in real-world classroom and research settings.The Linking Writing Processes to Writing Quality competition explored how students plan, revise, and manage their time during writing. By analyzing detailed keystroke logs from thousands of student essays, participants developed models capable of predicting overall writing quality from process-level features. This work provided new insights into the underlying cognitive and behavioral aspects of writing, offering potential for more personalized and actionable feedback for students and educators.The Human vs. AI Detection in Essay Writing competition addressed the growing challenge of distinguishing AI-generated text from human-written work. Using a dataset of student and AI-generated essays, competitors developed models that could reliably identify AI-generated content. These models support academic integrity and provide educators with tools to navigate the rapidly evolving landscape of digital writing while promoting authentic student work.ASAP 2.0 revisited automated essay scoring with updated datasets and contemporary machine-learning approaches. Competitors created models that accurately scored student essays while minimizing bias, reducing teacher workload, and enabling faster and more equitable feedback for students across diverse educational settings. This work supports scalable assessment practices that can improve student outcomes while maintaining fairness and reliability.Finally, the Personally Identifiable Information Identification competition focused on improving the safety and accessibility of educational data. Participants developed models to accurately detect and label sensitive information in student-written text, ensuring privacy protections while enabling the sharing and use of high-quality datasets for research and educational purposes. This work contributes to safer, more usable educational data resources for the broader research community.Across all four competitions, The Learning Agency Lab engaged thousands of participants worldwide, producing open-source, high-performing models that advance teaching, assessment, and data privacy in education. In addition to developing these tools, the Lab has continued to build partnerships with educational vendors to encourage the adoption of the algorithms created through the competitions. The Lab also continues to update the Learning Exchange platform, making the datasets, models, and related resources widely accessible to support ongoing research, innovation, and equitable educational practices.

  Expenses $795,343 · Grants $332,028 · Revenue $47,847

As filed on the 2024 return.

Officers and directors · 2024

As listed in Part VII of the 2024 return, in the order the filer printed them. Each return names its own; this is not a roster the organization keeps with us.

Compensation is as filed in Part VII: reportable pay from this organization and from related organizations, other compensation, and hours per week. The total, marked calculated, is our sum of the three dollar columns, with a blank column counted as $0; the form itself has no total. On a Form 990-EZ the columns are Part IV's instead, and on a Form 990-PF Part VIII's — compensation, contributions to benefit plans, and expense account — and the total sums those. Earlier years are each on their own filing page.

Filings

• 2024

  Revenue

  $810,580

  Expenses

  $864,599

  Contributions

  $687,992

  Grants paid

  $332,028

• 2023

  Revenue

  $1,150,141

  Expenses

  $1,286,492

  Contributions

  $1,110,949

  Grants paid

  $0

• 2022

  Revenue

  $1,601,616

  Expenses

  $1,276,616

  Contributions

  $1,592,328

  Grants paid

  $0

The four totals the IRS asks for on Part I of the Form 990, as filed. A dash means the figure was not present on the return — not that it was zero.

Other returns filed

• 2021 · 990

  Revenue

  $2,287,853

  Expenses

  $953,772

  Assets (EOY)

  $2,136,148

• 2020 · 990

  Revenue

  $874,948

  Expenses

  $823,830

  Assets (EOY)

  $1,519,783

• 2019 · 990-EZ

  Revenue

  $100,000

  Expenses

  $66,897

  Assets (EOY)

  $51,777

Nonprofiteer parses Forms 990, 990-EZ and 990-PF, in a window of recent tax years. These returns are recorded from the IRS e-file index, so their figures were extracted by that index rather than read from the return here — treat them as a different source from the filings above, not as more of the same record. A dash means the index carries no figure; Form 990-T listings carry none at all.

Grants made

• ThinkCERCAcom Inc

  $250,000

  2024 · User Approach Challenge / Dev MVP

• TeeRead Inc

  $82,028

  2024 · User Approach Challenge / Dev MVP

Where these grants went next →

Grants received

• VANDERBILT UNIVERSITY

  $664,681

  2023 · Research

• VANDERBILT UNIVERSITY

  $550,000

  2022 · Research

• FOUNDATION FOR EXCELLENCE INEDUCATION INC

  $100,000

  2022 · DATA SCIENCE EDUCATION

Other IRS files