Abstract & Details
Description
Award ID: 2627595
This FINDERS Foundry award introduces students to modern civil engineering tools that they rarely see in school, such as drones, laser scanners, and AI systems. This project will create an immersive learning environment where students can explore real 3D models of engineering sites. They will roleplay as surveyors, drone pilots, or engineers and learn how AI interprets images and measurements from the world. These activities will help students grow spatial reasoning skills and learn about new careers in engineering. Students, teachers, and families will help design the system so it reflects real interests and works well for both middle and high school students. By making engineering more handson and exciting, the project hopes to inspire students to explore STEM careers they may not have considered before. This FINDERS Foundry award co-designs an AIenabled surveying environments which include semantic segmentation for visible machine perception and a rolebased largelanguagemodel teaching agent. The project team co-develops wireframes, lowfidelity prototypes, and measurement tools while producing a roadmap for future development. While aligning the environment with engineering and CTE program pathways, researchers also analyze how immersive AI experiences influence spatial cognition and designing interactions help to reveal transparent machine reasoning for learners. Together the team prepares research protocols, evaluation measures, and essential go/nogo criteria for broader piloting. The planning work will define the foundations of an AIsupported learning system that blends machine perception, interactive reasoning, and handson engineering exploration. 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
This FINDERS Foundry award introduces students to modern civil engineering tools that they rarely see in school, such as drones, laser scanners, and AI systems. This project will create an immersive learning environment where students can explore real 3D models of engineering sites. They will roleplay as surveyors, drone pilots, or engineers and learn how AI interprets images and measurements from the world. These activities will help students grow spatial reasoning skills and learn about new careers in engineering. Students, teachers, and families will help design the system so it reflects real interests and works well for both middle and high school students. By making engineering more handson and exciting, the project hopes to inspire students to explore STEM careers they may not have considered before. This FINDERS Foundry award co-designs an AIenabled surveying environments which include semantic segmentation for visible machine perception and a rolebased largelanguagemodel teaching agent. The project team co-develops wireframes, lowfidelity prototypes, and measurement tools while producing a roadmap for future development. While aligning the environment with engineering and CTE program pathways, researchers also analyze how immersive AI experiences influence spatial cognition and designing interactions help to reveal transparent machine reasoning for learners. Together the team prepares research protocols, evaluation measures, and essential go/nogo criteria for broader piloting. The planning work will define the foundations of an AIsupported learning system that blends machine perception, interactive reasoning, and handson engineering exploration. 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
| Status | Active |
|---|---|
| Effective start/end date | 07/15/26 → 11/30/26 |
Active Fiscal Year
- FY2027
- FY2026
Start Fiscal Year
- FY2026
TIP Programs
- (FF) FINDERS FOUNDRY
Key Technology Areas
- Artificial Intelligence
- (confidence score: 94%)
Technology Foci
- Machine Learning Training Data
- (confidence score: 82%)
- Artificial Intelligence (excluding ML)
- (confidence score: 83%)
Congressional District at Award
- District n. 15 of Texas
Current Congressional District
- District n. 15 of Texas
United States
- Texas
Core Based Statistical Area (CBSA)
- McAllen-Edinburg-Mission, TX
County
- County: Hidalgo, TX
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