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Cooperative agreement Open

AI-Supported Project Based Learning

NCER
Program: Transformative Research in the Education Sciences
Award amount: $3,638,543
Principal investigator: Eric Klopfer
Awardee:
Massachusetts Institute of Technology (MIT)
Year: 2026
Award period: 3 years (09/01/2026 - 08/31/2029)
Project type:
Development and Innovation
Award number: R305T260046

Purpose

The purpose of AI Supported Project Based Learning (AI-PBL) is to enhance Project-Based Learning (PBL) in K-12 education by integrating AI-powered tools. The project aims to make PBL more accessible and effective by addressing the challenges teachers and students face in implementing and participating in PBL, such as generating driving questions, assessing student learning, and managing collaborative work. The project will develop and refine a tool called Collaborative Artificial Intelligence for Learning (CAIL) that supports both teachers and students in creating and managing high-quality PBL experiences.

Project Activities

First, the project will gather data on how teachers and students interact with early prototypes of the CAIL tool, focusing on the tool's interface and conversational agent. This feedback will guide further development and refinement. Then, researchers will conduct a preliminary implementation study in partner schools to evaluate the effectiveness of CAIL in real classroom settings. Then, researchers will conduct an efficacy study to assess CAIL's impact on PBL implementation by teachers, student collaborative learning, and student learning outcomes. Throughout all three years, the project will conduct ongoing feasibility and cost analyses to ensure the tool's practicality and sustainability.

Structured Abstract

Setting

The setting will be rural or urban schools in Indiana, Massachusetts, and Florida.

Sample

The students will be primarily high school students across six participating schools that vary widely in demographics, from primarily white and rural to 95% racial/ethnic minority and urban.

Research design and methods

First, researchers will gather data on how teachers and students interact with early prototypes of the CAIL tool, focusing on the tool's interface and conversational agent. This feedback will guide further development and refinement. Then, researchers will conduct a preliminary implementation study in partner schools to evaluate the effectiveness of CAIL in real classroom settings. Then, researchers will conduct an efficacy study to assess CAIL's impact on PBL implementation by teachers, student collaborative learning, and student learning outcomes. Throughout all three years, the project will also conduct ongoing feasibility and cost analyses to ensure the tool's practicality and sustainability.

Control condition

The comparison condition for the efficacy study will be PBL teachers using CAIL and those not using CAIL. Researchers will attempt to match across schools.

Key measures

Researchers will measure use of AI in PBL (treatment teachers) or PBL (comparison teachers) during classroom observations with the PBL Works Project Based Teaching Rubric. For the efficacy study, researchers will use the ATTARI-12 and AI Attitude Scale-4 to measure teacher and student attitudes toward AI, and the Teacher Self-Efficacy Scales to measure self-efficacy in teaching PBL. Student outcome measures will also include course grades through administrative data, collaboration using the ABCDEs of collaboration rubric, and the PBL Words critical thinking rubric.

Data analytic strategy

The research will employ a mixed-methods approach, using classroom observations, artifact interviews, and teacher and student surveys to gather data. The data will be analyzed using qualitative analysis software like Dedoose, and hierarchical linear modeling for the randomized controlled trial. RAND will conduct the evaluation, which will include formative feedback to the development team and a final efficacy study to measure the impact of CAIL on PBL implementation and student outcomes.

Cost analysis strategy

Researchers will conduct a cost analysis using the ingredients method, examining costs in terms of materials, personnel, responsible parties, and marginal units. The analysis will also consider cost-effectiveness, comparing costs to intervention effects.

People and institutions involved

IES program contact(s)

Courtney Pollack

Education Research Analyst
NCSER

Products and publications

ERIC Citations: Find available citations in ERIC for this award here.

Questions about this project?

To answer additional questions about this project or provide feedback, please contact the program officer.

 

Tags

Education TechnologyK-12 EducationStudentsTeaching

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Questions about this project?

To answer additional questions about this project or provide feedback, please contact the program officer.

 

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