The Research Coordination Network on Assessing and Predicting Job Outcomes in AI (RCN APJO-AI) brings together leaders across education, industry, government, and community-based organizations to build a shared, national understanding of AI jobs in a rapidly evolving labor market. The network examines what constitutes an “AI job,” identifies the skills and competencies required for AI-enabled work, and informs the development of programs and credentials aligned with emerging workforce needs.  

Supported by the U.S. National Science Foundation (NSF #2537092) and hosted by Howard University, this network is a collaboration with partners across the United States. The network coordinates national activities, including convenings, working groups, synthesis products, data resources, and a public knowledge-sharing platform as an effort to advance national capacity in AI workforce development and strengthen the U.S. innovation ecosystem. 

Purpose of the Network 

The RCN APJO-AI establishes a coordinated national effort to:

• Assess the evolving landscape of AI-related jobs

• Identify the skills and competencies required in AI-enabled work

• Align credentials, curricula, and training programs with real-time workforce needs

Core Focus of the Network

The RCN APJO-AI organizes its work around three central questions:

1. What defines an “AI job”? Assess and predict the evolving landscape of AI-related jobs to understand how AI-related roles are classified across sectors and occupations.

2. What skills are needed for AI jobs? Identify skills and human-centered competencies required in AI-enabled work that reflect employer demand.

3. How do we build AI credentials and curricula? Gather real-time workforce needs to inform the alignment of credentials, curriculum, and instructional models to emerging AI workforce needs.

The network will generate synthesis reports, convenings, and open resources that support educators, employers, and policymakers in making informed decisions about AI workforce preparation.