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Summary
U.S. Cooperative Extension provides useful, relevant, local, research-based, information to citizens throughout the country. Modern affordances like web searches, email, and instant messaging have built the expectation that information and services are available in real time and via the Internet–today’s primary source of information.
The local, nuanced, time-sensitive information that Extension and the Land Grant Universities provide exists, but can be difficult to find. Even more, the best resources are currently distributed across many data sources: e.g. Land Grant University information pages, Cooperative Extension websites and databases, Regional Integrated Pest Management Centers, Regional Rural Development Centers, and even eXtension’s massive “Ask an Expert” database of questions and answers.
This investigation looks at one solution to the problem of information fragmentation and “findability” of research-based answers with local relevance: the creation of a distributed Ask Extension data registry and search interfaces. This solution would pull together data sources from throughout the Extension ecosystem to answer questions directly and accurately and connect local citizens with their local Extension professionals, products, and services.
Advances in technology such as machine learning, Artificial Intelligence (AI), and related data tools present the possibility of capitalizing upon this opportunity, by offering smart and localized access to the addressable research-based information. In addition, they offer long-term scalability that will answer common questions immediately, providing 24/7/365 availability of Extension resources.
Read the full report here.