Are Nigerian Universities Preparing Students for the AI Economy?

Nigeria has a national plan for AI talent and a growing network of labs, bootcamps, and public–private programmes. 

But most university classrooms haven’t caught up yet. Policy momentum is real; classroom readiness is uneven.

In 2024, Nigeria released a National Artificial Intelligence Strategy led by NITDA/NCAIR, setting a vision to build skills, research capacity, and responsible AI governance. 

In 2025, NITDA followed up with an AI Transformation Roadmap focused on people, content, and technology,clear signals that the government expects universities to become talent engines, not spectators.

What campuses are doing today

The University of Lagos has an AI & Robotics Lab (AIROL) and hosts regular AI research events, even as it wrestles with funding constraints common across the system. 

The University of Ibadan will host Data Science Africa 2025, bringing continental researchers and industry to campus. 

American University of Nigeria has been named a learning hub for the national DeepTech Ready upskilling programme. 

Beyond campuses, Data Science Nigeria (now DSN AI) runs nationwide communities, meetups, and youth programmes that often fill curricular gaps.

The 3 Million Technical Talent (3MTT) programme sits alongside universities, funding large-scale training in software, data, cloud, cybersecurity, and explicitly AI/ML. 

A new DeepTech track launched in 2025 aims to upskill tens of thousands in data science and AI through successive cohorts, supported by partners in the tech ecosystem. 

These pipelines are already wired to placement and internships, something many departments still struggle to systematise.

Curricula: updated frameworks, slow adoption

The National Universities Commission replaced BMAS with CCMAS to make programmes more “21st-century,” and issued e-learning guidance for digital delivery. 

But CCMAS is a framework; AI-ready syllabi, MLOps courses, and industry capstones still depend on each university’s will, faculty capacity, and infrastructure. 

Many departments have introduced AI as a topic inside Computing and Engineering; far fewer have full tracks that reflect how AI is actually built and deployed today.

Where the gaps are (and why they matter)

1) Practical skills vs lecture halls. Employers need model development, data engineering, and MLOps skills,version control, notebooks, experiment tracking, deployment, and monitoring. Too many classes stop at theory or offline labs, not production AI workflows. Strategy documents themselves warn of a shortage of skilled AI professionals and call for stronger university partnerships.

2) Compute, data and labs. Many labs lack reliable GPUs, cloud credits, or curated local datasets in fields like health, agriculture, and energy. UNILAG’s experience, ambition outpacing support, is typical. Without accessible computing, students can’t progress beyond toy projects.

3) Faculty development. Cutting-edge AI moves fast; sabbaticals, industry externships, and incentives to publish or ship applied projects are rare. CCMAS can enable change; it doesn’t train lecturers by itself.

4) Industry linkages and IP. Consistent, credit-bearing internships, co-supervised theses with startups, and clear IP policies are still patchy, precisely the bridges that programmes like 3MTT and community groups try to build outside the university gate.

What “AI-ready” looks like in a Nigerian university (now)

A credible AI pathway in 2025 has four moving parts:

• Curriculum that mirrors practice: intro ML, deep learning, data engineering, responsible AI, and an end-to-end MLOps course using real tools (Git, containerisation, experiment tracking, CI/CD for models).
  
• Infrastructure that scales: pooled GPUs or cloud credits; a data hub with Nigerian use cases; and sandboxed environments students can access off-campus in line with NUC e-learning rules.
  
• Work-integrated learning: semester-long capstones with companies; joint supervision; hackathons judged by industry; pathways into 3MTT cohorts for extra depth.
  
• Faculty and community: funded training for lecturers; visiting-engineer programmes; and partnerships with DSN AI and DSA to keep content current and research connected to local problems.