Across many rural and resource-constrained communities in the United States (including agricultural regions, Appalachian and coal country areas), resident-led groups and civic organizations work to address local concerns ranging from water safety and soil health to food production and land conditions. Yet gathering and acting on local data remains a persistent challenge. Existing tools for such tasks often assume reliable internet connectivity, use data platforms owned and managed by outside companies, and need specialized technical expertise that resource-constrained communities simply do not have. This leaves dedicated community groups without practical tools that work under real-world field conditions, regardless of how committed or knowledgeable they are about their local needs. This project develops a new class of low-cost, portable Edge Artificial Intelligence (EdgeAI) tools that embed machine learning directly into tiny, inexpensive computing devices that can operate without internet access or specialized equipment. These EdgeAI tools will enable grassroots communities with limited technical infrastructure to understand local conditions by creating, adapting, and managing their own data systems. Field research with civic organizations in Appalachian Tennessee will guide the design of these tools and generate design principles applicable to similar communities nationwide. By placing practical EdgeAI tools in the hands of community members, the project strengthens local capacity to detect and respond to concerns about issues such as water safety, soil health, and food production, supporting the health and economic welfare of communities that currently lack access to advanced technology. The project also engages undergraduate and K-12 students from rural and other low-infrastructure settings in hands-on, place-based computing, helping prepare the next generation of technologists who understand and serve the needs of their communities. This project investigate