Skip to main navigation Skip to search Skip to main content

Planning: NSF FF: Planning: Promoting Success in Algebra by Integrating Best Practices in Small-Group Learning with AI

Project: Research

Abstract & Details

Description

Award ID: 2627571

This FINDERS Foundry award accelerates students success in the pivotal STEM pathway of Algebra by improving practices of small-group learning for educators and learners. While many students struggle in Algebra, diminishing longterm STEM engagement, teachers too face high workloads when implementing research-based Cooperative Learning approaches to scaffold student learning. Through co-design of a platform that streamlines group formation, supports collaborative learning, and allows teachers to continuously monitor progress and Cooperative Learning routines. Students receive timely feedback while teachers have real-time insights to enhance learning. The platform integrates Cooperative Learning routines with an Algebraspecific AI tutor to provide realtime feedback to student groups while teachers use a dashboard to identify misunderstandings and deliver timely support. By lowering barriers to highquality smallgroup instruction, the project improves Algebra learning and strengthen students persistence in STEM. This FINDERS Foundry award identifies the technical design required to reduce planning and logistical burdens teachers face when implementing Cooperative Learning in Algebra to accelerate student knowledge acquisition and enhance instructional time. By identifying essential Cooperative Learning design features, modeling teacher workflow requirements, and specifying the technical infrastructure the system offers realtime AIsupported feedback loops to learners. Integrating intelligent groupmanagement features with an AI tutor capable of delivering formative feedback, diagnoses patterns of student misunderstanding, routing actionable insights to teachers with data to provide a first tier of distributed support. The project outlines metrics for evaluating instructional value, teacher workload efficiency, and student learning outcomes to result in a highfidelity framework for AIsupported Cooperative Learning that enhances Algebra instruction while preserving teacher oversight and aligning with evidencebased cooperative structures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Lindsay Portnoy
StatusActive
Effective start/end date07/15/2610/31/26

Active Fiscal Year

  • FY2027
  • FY2026

Start Fiscal Year

  • FY2026

TIP Programs

  • (FF) FINDERS FOUNDRY

Key Technology Areas

  • Artificial Intelligence
  • (confidence score: 95%)

Technology Foci

  • Artificial Intelligence (excluding ML)
  • (confidence score: 92%)

Congressional District at Award

  • District n. 04 of Oregon

Current Congressional District

  • District n. 04 of Oregon

United States

  • Oregon

Core Based Statistical Area (CBSA)

  • Eugene-Springfield, OR

County

  • County: Lane, OR

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint. Learn more about Elsevier's Fingerprint Engine here: https://beta.elsevier.com/products/elsevier-fingerprint-engine