Partnering With AI To Develop Relational Maps

Last Updated 11/22/2023

In 2024, the Learn-& Work Ecosystem Library will use relational maps to complement its narrative descriptions of key searchable artifacts, such as Key Components of the learn-and-work ecosystem, Topics, Initiatives, and Organizations. The maps are developed by integrating manual data tagging with inferred AI-driven relations. This work is performed under an open licensing agreement that allows the Library to train and continuously refine the AI model.

The relational maps at the Library are depicted at the end of a narrative artifact. The map depicts how a particular Initiative is related to various Organizations working in that space, is related to Glossary Terms, is related to other Initiatives, and is related to other Topics. Live links to the items on the map are provided.

Examples of Prototype Maps Resulting from Initiative Search

Example Prototype Map 1: Quality and In-Demand Non-Degree Credentials Framework – Colorado
Example Prototype Map 2: Microcredentials Pilot in Higher Education — Australian Government Department of Education
Example Prototype Map 3: SkillsFWD

Example of Map Resulting from Key Component Search

Example Prototype Map 4: Verifications / Recordkeeping

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For the ecosystem to function effectively, all parts of the system must be connected and coordinated.

Organizations (275)