Contract Open

Transforming STEM Learning: Local Ecosystems as Classrooms

NCER

Program: Small Business Innovation Research

Award amount: $250,000

Project director: Dietrich Ruehlmann

Awardee:

GaiaXus LLC

Year: 2025

Award period: 8 months (09/12/2025 - 05/11/2026)

Project type:

Phase I Development

Contract number: 91990025C0081

Purpose

The purpose of this project is to advance the development of an integrated education technology platform that enhances science learning through data-driven, community-connected experiences. The system is designed to help educators create modular, standards-aligned laboratory activities that combine authentic environmental data with AI-assisted instructional tools.

By integrating artificial intelligence into the lesson-building process, educators can generate contextual background information and assessments tailored to their learning objectives, improving both efficiency and instructional relevance. The platform further supports inquiry-based and place-based learning by incorporating diverse student-generated data and connecting classrooms with local partners and citizen scientists.

Overall, the project aims to demonstrate how the thoughtful use of AI, data systems, and participatory design can strengthen STEM education, promote data literacy, and expand access to experiential learning across diverse school environments.

Project Activities

The project will advance the development and validation of a digital education platform that integrates artificial intelligence, environmental data, and participatory design to enhance science learning. Core activities include iterative design and testing of the system’s educator tools, data architecture, and community engagement components.

Educators will use the platform to design modular, standards-aligned laboratory activities connected to authentic environmental data. AI-assisted features will support teachers in generating contextual background content, quizzes, and formative assessments aligned with learning objectives. This process will be evaluated through structured educator feedback sessions, classroom trials, and usability testing.

Parallel work will expand the platform’s capacity to incorporate student-generated media such as images and text, and to link classroom projects with community scientists and local partners. Together, these activities demonstrate a scalable model for integrating AI-driven content creation, data systems, and experiential learning to promote data literacy, collaboration, and STEM engagement across diverse educational settings.