BILT Project Lessons for Ghana’s AI Skills Training

Ghana’s skills gap isn’t a mystery. It’s visible on job sites, in workshops, and inside growing SMEs that can’t find technicians who can troubleshoot a modern machine, interpret a basic dashboard, or document work properly. The uncomfortable truth is that many training systems still treat digital tools as “extra”—when employers now treat them as standard.

That’s why the UNESCO-UNEVOC BILT project (Bridging Innovation and Learning in TVET) is worth paying attention to. BILT isn’t just another education programme with nice slogans. It’s a practical model for how countries and training institutions collaborate to modernise technical and vocational education and training (TVET) using digital learning—and by extension, AI-enabled learning tools.

This post connects the BILT idea to the reality on the ground in Ghana: TVET providers, apprenticeship systems, and SMEs that need job-ready talent. I’ll also show how SMEs can benefit directly—because stronger skills training doesn’t only help students; it reduces hiring risk, rework, and operational chaos for small businesses.

What the BILT project signals (and why Ghana should care)

BILT signals that TVET modernisation works best when it’s coordinated across institutions, employers, and policy—rather than left to individual schools to “figure it out.” UNESCO-UNEVOC’s work around BILT focuses on improving how training systems adopt innovation and digital learning in a structured way.

Here’s the part people miss: when global education projects succeed, it’s rarely because of a fancy platform. It’s because they build shared methods—common curriculum approaches, employer involvement, and repeatable models that can scale.

For Ghana, the relevance is immediate:

• TVET is expanding, but quality and consistency vary widely.
• SMEs are hiring into roles that now require digital literacy plus hands-on skill.
• AI tools are becoming everyday workplace tools (for writing, troubleshooting, customer support, inventory), yet training often doesn’t teach how to use them responsibly and productively.

A good collaboration model like BILT provides a blueprint: align training content with labour-market needs, enable digital learning across institutions, and create feedback loops with employers.

The myth: “Digital learning is only for universities”

Digital learning isn’t a university-only thing. In fact, TVET may benefit more quickly because many competencies are modular and task-based: diagnostics, safety checks, quality control, customer communication, basic reporting.

If a trainee can learn how to:

• record a job card clearly,
• follow a standard checklist,
• interpret simple sensor data,
• generate a maintenance report,

…then they’re already working in a way that supports AI-assisted workflows later.

AI-driven digital learning in TVET: practical use cases that fit Ghana

AI in vocational training isn’t about replacing instructors; it’s about scaling practice, feedback, and documentation. When done well, it reduces the “one instructor, 40 learners” problem and gives trainees more guided repetition.

Below are AI-driven learning use cases that match Ghana’s TVET realities—especially where equipment is limited and class sizes are large.

1) AI-assisted coaching for workplace writing and documentation

Most SMEs quietly lose money due to poor documentation: unclear job notes, missing stock records, inconsistent customer follow-ups, and bad handover between shifts.

AI writing assistants can help trainees practice:

• job-card writing (problem, diagnosis, action, parts used, next steps)
• incident reporting and safety logs
• customer messages that are polite and clear
• basic procurement requests and inventory updates

This matters because good documentation is a technical skill now. In many trades, it’s the difference between “we fixed it” and “we can prove what we did and repeat it.”

2) Simulated troubleshooting (when equipment is scarce)

When institutions lack enough modern equipment, students get fewer repetitions. AI-based simulations and scenario prompts can fill part of that gap:

• “The machine vibrates after 10 minutes—what are the top 5 causes?”
• “The inverter shows error code X—what steps do you take first?”
• “A customer reports intermittent power—how do you isolate the fault?”

Even without advanced VR, text-based scenario practice improves diagnostic thinking, especially when paired with instructor review.

3) Personalised practice plans and quick feedback

In large classes, fast feedback is rare. AI can support:

• quiz generation from lesson notes
• quick marking for objective items
• targeted practice based on weak areas (e.g., measurement conversion, tool selection, safety steps)

The instructor remains in control, but learners get more practice cycles per week.

4) Micro-credential pathways tied to SME needs

BILT’s broader idea—bridging innovation and learning—pairs well with micro-credentials. SMEs don’t always need a 2-year programme; they sometimes need a person who can do a narrow job well.

Examples of short, employer-friendly skill badges:

• “Basic solar installation documentation and safety checks”
• “Refrigeration fault isolation and service reporting”
• “Fabrication measurement, costing, and customer quoting”

AI tools can help standardise training materials and assessment rubrics across multiple centres.

A collaboration model Ghana can copy: “BILT-style” partnerships

If Ghana wants AI-driven vocational training that sticks, the winning approach is partnership-first, platform-second. BILT highlights global collaboration; Ghana can adapt that mindset locally.

A practical structure that works:

Build a three-sided agreement: TVET + SMEs + enablers

• TVET institutions provide training delivery, assessment, learner support.
• SMEs provide real work scenarios, equipment exposure, and feedback on competence.
• Enablers (industry associations, local government, NGOs, telcos, edtech providers) support connectivity, devices, content development, and trainer upskilling.

The output shouldn’t be a “pilot that ends.” It should be a repeatable model with a shared calendar, shared outcomes, and shared data.

Standardise what “job-ready” means

Most skills conversations stay vague. Don’t.

Define job-ready with measurable behaviours, such as:

1. Completes a task using a checklist with zero missed safety steps.
2. Produces a clear service report in under 10 minutes.
3. Communicates diagnosis to a customer in plain language.
4. Uses a digital tool to record parts used and update stock.

Once these are clear, AI tools can support practice and evaluation.

Train the trainers (and pay attention to incentives)

If instructors are overworked and under-supported, digital learning becomes “extra work.” What I’ve found works better is:

• give trainers ready-to-use lesson templates and assessments
• set simple policies for AI use (what’s allowed, what’s not)
• provide time for peer-learning among trainers

When trainers see AI as a tool for reducing repetitive tasks (marking, drafting rubrics, generating practice questions), adoption improves.

How SMEs in Ghana benefit directly (and how to get involved)

SMEs benefit when training systems produce workers who can communicate, document, and learn fast—not only “do the work.” That’s the connection to this series, Sɛnea AI Reboa Adwumakuo Ketewa (SMEs) Wɔ Ghana: AI supports productivity, but people still need the right habits and skills to use it.

Here’s how SMEs can turn this into a practical advantage.

Offer “real work” learning tasks (not just attachment)

Many attachments are unstructured: learners watch, sweep, and disappear.

A better approach is to host learners with three structured tasks per week, for example:

• write one customer-ready service report
• create a parts list and basic cost estimate for a job
• document a safety checklist for a recurring task

Then ask a supervisor to score them quickly (even 5 minutes). That’s enough to build competence fast.

Use AI tools internally to standardise operations

If your business doesn’t document work consistently, learners won’t learn it.

Simple AI-supported templates you can adopt:

• job card template
• quotation template
• customer follow-up message template
• weekly inventory update template

The benefit is immediate: less confusion, fewer repeated mistakes, and easier onboarding.

Co-design micro-credentials with a nearby training centre

SMEs can partner with local TVET providers to design a short module that matches real needs. Pick one high-value pain point—like faulty documentation, inconsistent quoting, or repeated rework.

A 4–6 week module with clear performance checks can be more useful than vague “skills training.”

One-liner worth keeping: If you can’t measure job readiness, you can’t train for it.

Common questions people ask about AI and vocational training

“Won’t AI encourage cheating?”

It can—if assessments only reward perfect text. The fix is straightforward: evaluate process and performance.

• Assess practical work on-site.
• Use oral explanations (“walk me through your diagnosis”).
• Require photo evidence and checklists for tasks.
• Grade documentation for clarity and accuracy, not fancy language.

AI becomes a learning aid, not a shortcut.

“What if connectivity is poor?”

Design for low-bandwidth reality:

• downloadable lesson packs
• offline-first mobile content
• WhatsApp-based assignments (structured, not random)
• periodic sync points at centres with stronger internet

A digital learning plan that assumes perfect internet will fail outside major areas.

“Isn’t this too expensive for TVET?”

Not if you prioritise the right costs. The biggest budget killers are shiny tools without adoption.

Start with:

• trainer capacity building
• structured content and assessments
• basic devices where needed
• clear rules for AI usage

Then add more advanced platforms once the system works.

What to do next in Ghana (a practical 90-day action plan)

The fastest path is a small, disciplined pilot that’s designed to scale. If you’re a training provider, NGO, school leader, or SME owner, here’s a workable 90-day plan:

1. Pick one trade area (auto electrical, welding/fabrication, hospitality, HVAC, electrical installation).
2. Define 6 job-ready competencies with local SMEs (observable, measurable).
3. Create 12 AI-supported practice tasks (scenario prompts, checklists, documentation templates).
4. Train 10 instructors/supervisors on using the materials and marking consistently.
5. Run the pilot with 30–60 learners and collect weekly feedback.
6. Publish results internally: pass rates, employer satisfaction, documentation quality.

If the pilot improves documentation and reduces rework during attachment, SMEs will stay involved—because it saves them money.

The BILT project is a reminder that TVET reform doesn’t need to be isolated or slow. Collaboration plus practical digital learning methods can move the needle quickly, especially when AI tools support repetition and feedback.

This series is about how AI helps SMEs in Ghana run better—writing clearer business documents, improving customer communication, and managing records without a big staff. Stronger AI-ready vocational training is the upstream version of the same goal: people who can work, document, and improve.

What would change for your business—or your training centre—if every trainee could produce a clear service report and a consistent quote by the end of their first month?