Howard University Leads NSF-Funded Network to Forecast AI Job Market Trends

Howard University Leads National Effort to Predict AI Job Market Trends

A Howard University-led consortium is building a national Research Coordination Network (RCN) to study the current state of AI jobs and forecast what's next. The two-year project is funded by a nearly $500,000 grant from the National Science Foundation and led by Howard mathematics professor Dr. Talitha Washington.

The initiative will connect educators, industry leaders, government agencies, and workforce innovators to define AI roles, identify required skills, and inform credentials and curricula. The goal: create a shared, evidence-based view of AI work to help talent, employers, and institutions move in sync.

What the Network Will Answer

• What qualifies as an AI or AI-adjacent job?
• Which AI skills are actually needed on the job?
• How should we build AI credentials and curricula that map to real roles?

Why It Matters for IT, Development, and Research

Titles vary. Requirements are inconsistent. Hiring signals are noisy. This RCN aims to bring clarity so teams can hire better, professionals can upskill with purpose, and universities can align programs with market reality.

Expect clearer definitions for roles such as AI research scientist, robotics engineer, machine learning engineer, AI ethics specialist, and UX designer working with AI systems. That clarity reduces guesswork for job seekers, hiring managers, and curriculum designers.

What the RCN Will Produce

• Grounding documents that define AI and AI-adjacent roles
• Synthesis reports and workshop proceedings
• Curated labor data dashboards tracking AI job trends
• A public-facing knowledge base website
• National convenings and workshops to vet findings with stakeholders

At the end of the two-year effort, the team will host a synthesis capstone workshop to consolidate findings and recommendations.

Who's Leading and Collaborating

The project is led by Dr. Talitha Washington, executive director of Howard's Center for Applied Data Science and Analytics and co-chair of the university's AI Advisory Council. Co-principal investigators include Enrico Pontelli (New Mexico State University), Siobahn Grady (North Carolina Central University), and George Brown (Houston Community College).

Washington notes the focus is practical: create shared references so people across sectors know what AI jobs are and how to prepare. This is a coordination effort aimed at action, not a traditional research study.

How You Can Use This Work

• For engineering leaders: align job descriptions and hiring rubrics with the network's role definitions and skill maps.
• For developers and researchers: target skills that the dashboards and reports show are in demand; track credential pathways as they emerge.
• For faculty and program directors: update syllabi to reflect validated competencies; plug into workshops to stress-test course outcomes.
• For workforce teams: partner early to shape standards that fit real projects and compliance needs (including ethics and risk).

Part of Howard's Broader AI Initiative

This project complements Howard's push to advance AI scholarship and prepare students for careers in AI-related fields. Washington's team is also hosting a virtual workshop for Howard faculty and staff, "AI and the Future of Higher Education," on Sept. 17, to help educators translate these insights into classrooms and programs.

Stay Informed and Prepare Your Skills

• Learn about the NSF's Research Coordination Networks program: NSF RCN Program
• Explore practical AI upskilling paths by job and skill: AI courses by skill and popular AI certifications

Bottom Line

AI work needs shared definitions, verified skills, and clear credential paths. This national network, led by Howard University and Dr. Talitha Washington, is set up to deliver exactly that-so teams can hire with confidence, professionals can grow with intent, and curricula can meet real demand.