WASSCE Performance: Stop Blaming Licensure, Start Fixing Gaps

A single talking point has dominated the conversation after the latest WASSCE results: “Teachers failed licensure exams, so students failed WASSCE.” It’s a neat headline. It’s also a weak explanation.

Here’s what bothers me about that narrative. It treats WASSCE performance like a courtroom verdict—find the culprit, assign blame, move on. But education doesn’t work like that. WASSCE outcomes are the final printout from years of learning conditions: time on task, resources, class sizes, attendance, feedback cycles, language proficiency, assessment quality, and yes, teacher capacity. Reducing all of that to licensure exam performance is a policy shortcut.

This is part of the “AI ne Adwumafie ne Nwomasua Wɔ Ghana” series because Ghana doesn’t just need louder debates. We need better diagnosis. And AI—used responsibly—can help schools and education leaders measure learning gaps early, support teachers with practical tools, and focus intervention where it actually changes results.

The “licensure explains WASSCE” story is too tidy

The direct answer: Teacher licensure results can’t, by themselves, explain WASSCE performance trends. Licensure is a single filter at one point in time; WASSCE is the outcome of a long pipeline.

The RSS article (“The Licensure Fallacy: A misplaced narrative on WASSCE performance” by Ebenezer Afanyi Dadzie) pushes back on the claim that student underperformance is a direct consequence of teachers failing licensure examinations. That pushback is fair. The logic chain is shaky:

• Licensure exams test teacher readiness, not ongoing classroom effectiveness.
• WASSCE tests student mastery across multiple subjects after years of schooling.
• Between those two are dozens of variables that can amplify or erase the effect of any single factor.

A “tidy” narrative is politically attractive because it offers an easy lever: “Fix licensure, fix WASSCE.” But when the diagnosis is wrong, the treatment wastes time.

A simple rule: if a policy explanation fits in one sentence, it’s probably incomplete.

What a more honest explanation looks like

WASSCE performance is usually shaped by a stack of issues, not one.

In many Ghanaian schools, you’ll hear familiar constraints:

• Large class sizes that make marking and feedback slow
• Late syllabus completion (especially in core math and sciences)
• Mismatch between classroom tests and WASSCE-style questions
• Limited revision time and weak exam technique training
• Teacher workload that forces “coverage” over deep understanding

Licensure might be part of the wider teacher-quality picture, but it’s not a credible standalone cause.

Most systems fail because they diagnose late

The direct answer: WASSCE is a late-stage signal; the real opportunity is identifying gaps in JHS and early SHS.

WASSCE results come out when the learning has already happened (or didn’t happen). That’s why the conversation often turns into blame. People are reacting to a fire after the building has burned.

The better approach is operational:

1. Detect learning gaps early (weeks, not years)
2. Target support (which students, which topics)
3. Track progress with short feedback loops
4. Support teachers with time-saving tools and better assessment design

This is where AI can matter in a practical, non-hype way.

A school-based reality check (what actually breaks results)

If you want a strong predictor of WASSCE outcomes, look for whether a school has these habits:

• Weekly or bi-weekly topic mastery checks
• Structured remediation (not “extra class” as punishment)
• Data that shows which sub-topics are failing (e.g., quadratic equations vs. algebra generally)
• Regular practice with WASSCE-like items and mark schemes

When these habits are missing, students can appear “fine” until the final exam exposes the gaps.

How AI helps Ghanaian schools without blaming teachers

The direct answer: AI improves WASSCE performance by shortening feedback loops—so teachers see who is stuck, on what, and why, early enough to act.

Within the “AI ne Adwumafie ne Nwomasua Wɔ Ghana” theme, the point isn’t to replace teachers. It’s to reduce friction: less time spent on repetitive tasks, more time spent on teaching and targeted support.

1) AI-assisted diagnostics: from broad scores to specific gaps

Many schools track performance as “Class average is 43%.” That number is almost useless. You can’t remediate a percentage.

An AI-supported assessment workflow can:

• Break tests into skills maps (e.g., English: comprehension inference, summary, grammar agreement)
• Generate a gap report by student and by topic
• Recommend a remediation set (5–10 questions) focused on the exact weakness

Even with basic tools, the effect is powerful: teachers stop guessing.

Snippet-worthy truth: You don’t improve what you can’t see. AI makes learning gaps visible in time.

2) Better practice questions that match WASSCE standards

A common classroom problem is poor alignment: class exercises are too simple, while WASSCE questions test multi-step reasoning.

AI can help teachers draft:

• WASSCE-style multiple-choice items with plausible distractors
• Structured theory questions with marking guides
• Mixed-topic revision sets that build exam stamina

The teacher remains the final editor. That matters. Local context, syllabus coverage, and fairness still require human judgment.

3) Personalized learning without “one teacher per student”

Personal tutoring is effective and expensive. Ghana can’t staff a personal tutor for every learner. But we can get closer to personalization.

AI-enabled learning support (even as simple as guided practice prompts) can:

• Offer step-by-step hints instead of just “wrong”
• Provide Tw i / English explanations where appropriate for comprehension support
• Adjust difficulty after each attempt

This is not magic. It’s just a more efficient feedback system.

4) Teacher support: planning, feedback, and time savings

If teachers are overwhelmed, quality drops—regardless of licensure.

Practical AI use cases that directly help teacher effectiveness:

• Drafting lesson plans aligned to learning objectives
• Creating rubrics and quick marking schemes
• Producing short remedial notes for weak topics
• Summarizing common errors from a set of scripts

When teachers get time back, students benefit.

What policymakers and school leaders should measure instead

The direct answer: Shift from symbolic debates to measurable learning operations—time on task, syllabus coverage, formative assessment frequency, and remediation effectiveness.

If the public conversation stays stuck on licensure blame, we’ll miss what’s measurable and fixable.

Here are school- and district-level indicators that are more actionable than “licensure pass rate”:

School-level metrics (simple, high value)

• Syllabus completion rate by subject (by week, not end-of-term)
• Number of formative checks per month (per class)
• Remediation participation (who attended, who improved)
• Mock-to-WASSCE gap analysis (which topics consistently drop)

Teaching and learning quality signals

• Average feedback turnaround time on assignments
• Student attendance and lateness patterns (by term)
• Question-quality audit: percentage of items requiring multi-step reasoning

If you want better WASSCE performance, these are the levers.

A practical “AI + School” plan for Term 2 (Jan–Apr 2026)

The direct answer: Run a 12-week cycle focused on diagnostics, remediation, and exam alignment—using AI as the support engine.

December WASSCE debates fade quickly. January is when schools can actually change outcomes. Here’s a plan that fits the Ghana school calendar rhythm after the holidays.

Week 1–2: Baseline and gap mapping

• Short diagnostic tests in core subjects
• Tag items by topic and skill
• Produce class and student gap reports

Week 3–8: Targeted remediation

• Two remediation sessions per week for bottom skill clusters
• AI-assisted practice sets (teacher-reviewed)
• Track mastery every two weeks

Week 9–12: WASSCE alignment and exam technique

• Mixed-topic timed practice
• Mark scheme training (students learn how marks are earned)
• Focused writing practice for English (planning, coherence, time management)

This is where AI fits naturally: generating practice items, summarizing errors, and creating differentiated exercises fast.

“People also ask” (and straight answers)

Does teacher licensure matter at all?

Yes. Licensure can help standardize entry into the profession. But it’s not a sufficient explanation for WASSCE outcomes, and it won’t fix weak learning operations inside schools.

Will AI raise WASSCE scores by itself?

No. AI is a tool, not a strategy. Scores rise when schools use AI to tighten feedback loops, improve practice quality, and run consistent remediation.

What’s the risk of using AI in schools?

The main risks are poor data privacy, over-reliance, and low-quality content. The solution is governance: approved tools, teacher oversight, and clear rules on student data.

The better narrative Ghana needs

The direct answer: Stop treating WASSCE performance as a blame contest; treat it as a measurement problem with fixable learning gaps.

Ebenezer Afanyi Dadzie’s point about the “licensure fallacy” lands because it calls out a habit in our public debate: we confuse a convenient story for a true diagnosis. If we keep doing that, we’ll keep cycling through the same disappointment every results season.

The next step is practical: schools, parents, and education leaders should ask for evidence of learning progress before WASSCE, not just explanations after it. Within the AI ne Adwumafie ne Nwomasua Wɔ Ghana series, I’ll keep pushing this stance: AI is most valuable when it helps teachers teach better and helps students practice smarter—early enough to matter.

If Ghana focused on faster feedback, targeted remediation, and WASSCE-aligned practice—supported by AI tools—what would our results look like a year from now?