WASSCE Results: Beyond Teacher Licensure, Use AI

A single story has hijacked the WASSCE conversation: students did poorly because teachers failed the licensure exam. It’s a neat headline. It’s also a policy trap.

If we treat licensure as the main explanation for WASSCE performance, we’ll spend 2026 “fixing” the wrong problem—while classrooms keep struggling with overcrowding, uneven lesson quality, weak feedback loops, and limited learning support at home. This matters for parents, school leaders, and policymakers because WASSCE isn’t just an exam; it’s a national pipeline into tertiary education and the workforce.

Ebenezer Afanyi Dadzie’s argument—captured in the RSS summary under “The Licensure Fallacy”—pushes back against that simplistic framing. I agree with the core point: licensure may be useful as a gatekeeping tool, but it’s a weak explanation for system-wide exam outcomes. If we want better results, we need better teaching support, better learning practice, and better data.

Here’s the practical pivot for this series, “AI ne Adwumafie ne Nwomasua Wɔ Ghana”: AI can help Ghana shift from blame-based reform to support-based reform—support that reaches teachers and students every week, not just at exam time.

The licensure narrative is attractive—and misleading

Licensure is being used as a convenient scapegoat because it sounds like accountability. A licensure exam creates a simple “pass/fail” story, and public debates love simple stories.

But WASSCE performance is an output of many inputs:

• Curriculum pacing vs actual contact hours
• Quality and consistency of lesson delivery across schools
• Availability of textbooks, past questions, labs, and ICT tools
• Student attendance, hunger, household workload, and motivation
• Assessment habits (how often students practise exam-style questions)
• Language proficiency (especially in subjects that are text-heavy)

Licensure can tell you something narrow: whether a teacher met a minimum bar at a point in time. It doesn’t tell you whether teachers are getting ongoing coaching, whether students are practising enough, or whether schools have the resources to teach the syllabus well.

Blaming licensure for WASSCE outcomes is like blaming a driving test for road traffic jams. The test matters, but it’s not the traffic.

What a better question sounds like

A more serious policy question is: “What are the most common learning gaps showing up in WASSCE scripts, and what classroom routines would close them fastest?”

That question forces us to look at evidence: item analysis, topic-level weaknesses, time-on-task, and whether students are receiving timely feedback. It also opens the door to tools—especially AI tools—that can scale support without waiting for perfect staffing levels.

What actually drives WASSCE performance (and where Ghana can act)

WASSCE performance improves when students get frequent practice, fast feedback, and clear explanations—especially in weak-topic areas. That’s not theory; it’s the basic mechanics of learning.

Below are the drivers I’ve seen repeatedly in Ghanaian classrooms (and in education systems globally), and how they show up during WASSCE season.

1) Low-frequency assessment and late feedback

Many students meet serious exam-style questions too late—sometimes only in the final term. When feedback arrives weeks later, it’s already stale.

Fix: weekly quizzes, short writing tasks, and structured marking rubrics.

2) Uneven instructional quality across schools

Even with the same syllabus, two schools can deliver totally different learning experiences. Some teachers have strong subject mastery and good pacing; others struggle—often without support.

Fix: shared lesson plans, peer observation, micro-coaching, and targeted CPD that’s tied to classroom evidence.

3) Resource constraints and time poverty

Overcrowded classrooms, limited teaching materials, and teachers handling heavy workloads reduce time for differentiated instruction.

Fix: tools that reduce preparation burden and make revision more efficient for students.

This is where the “AI ne Nwomasua wɔ Ghana” thread becomes practical: AI can strengthen the routines that matter most—practice, feedback, and teacher support—without waiting for a perfect system.

How AI supports teachers beyond licensure

AI doesn’t replace teacher training; it makes training and daily teaching support usable at scale. Licensure is a checkpoint. AI can be the weekly support system.

Here are concrete, Ghana-friendly uses that work even when budgets are tight.

AI as a lesson co-planner (time saved is quality gained)

Teachers often spend evenings creating notes, examples, and exercises. AI can reduce that workload and improve structure.

Practical outputs an AI assistant can produce in minutes:

• 40-minute lesson plan aligned to the SHS syllabus
• Starter activity, guided practice, and exit ticket
• Differentiated exercises (basic, standard, advanced)
• Marking scheme for short answers

The benefit is not “fancy tech.” It’s more time for actual teaching and remediation.

AI as a formative assessment engine (practice + feedback)

Frequent practice is the closest thing to a legal “cheat code” for WASSCE. AI tools can generate topic-based questions and provide immediate explanations.

Examples:

• Mathematics: generate 10 questions on simultaneous equations, then show step-by-step solutions
• Integrated Science: short concept checks with explanations for wrong options
• English Language: writing prompts + feedback on coherence and grammar

For schools worried about accuracy: teachers stay in charge. AI drafts; teachers verify.

AI as a teacher coach (micro-CPD that fits real life)

Traditional workshops are often one-off. Teachers go, they clap, they return to the same constraints.

AI can support “micro-CPD”:

• Quick refreshers on tricky concepts (e.g., mole concept, vectors, essay structure)
• Classroom management strategies for large classes
• Differentiation ideas for mixed-ability students

A solid model is: 10 minutes of targeted learning for the teacher, 40 minutes better teaching for students.

Licensure checks entry. Coaching builds competence. Ghana needs both, but coaching changes outcomes faster.

How AI supports students (especially those without extra tuition)

AI can reduce inequality in exam preparation by giving students a reliable study partner. Not a perfect one—reliable.

That matters in December 2025 because many families are doing end-of-year budgeting, paying fees, and choosing between extra classes and other necessities. If a student can’t afford consistent tuition, structured AI-supported revision can fill part of that gap.

Personalized revision plans that don’t waste time

Many students revise randomly. AI can help them work systematically:

• Identify weak topics based on quiz results
• Build a 4–6 week revision timetable
• Mix subjects to avoid burnout

Step-by-step explanations in local study realities

Students often need explanations that match their context—simple language, familiar examples, and clear steps. AI can re-explain the same concept multiple ways without embarrassment.

That’s a big deal in classrooms where students fear “wrong answers” and keep quiet.

Past questions, but smarter

Students love past questions, but they often repeat them without learning the underlying skill.

AI can:

• Group past questions by topic and skill
• Explain the pattern behind common question types
• Provide “similar but new” practice items to prevent memorising answers

Guardrails Ghana should insist on (so AI helps, not harms)

AI in education works when schools set rules early. If we ignore guardrails, we’ll get cheating scandals, misinformation, and distrust.

Here are non-negotiables I’d push for in 2026 adoption plans:

1) Academic integrity rules that students understand

Make it plain:

• AI can be used for practice and explanations
• AI-generated text can’t be submitted as a student’s own work
• Teachers may require drafts, outlines, and oral defenses

2) Teacher verification and content alignment

AI sometimes makes confident mistakes. Schools should standardise a routine:

• Teacher checks solutions before distributing
• Align questions to the Ghana syllabus and WASSCE style
• Keep an “approved bank” of AI-assisted materials

3) Data privacy and safe use

Students shouldn’t be pushed into sharing sensitive data. Keep usage simple:

• No uploading of personal documents unless necessary
• Use school-managed accounts where possible
• Clear policy for minors and parental consent

A realistic 90-day plan for schools and districts

You don’t need a national overhaul to start seeing benefits. A 90-day pilot can produce measurable changes in revision discipline and classroom assessment.

Days 1–14: Pick 2 subjects and set routines

• Choose two high-impact subjects (often Mathematics + English or Science)
• Agree on weekly quiz cadence (e.g., every Friday)
• Train a small teacher team to generate and vet questions with AI

Days 15–60: Build a feedback loop

• Use a simple tracker: topic, score, common errors
• Run 20-minute remediation sessions for the bottom 30% of students
• Create a shared folder of vetted explanations and marking guides

Days 61–90: Measure and decide

Track:

• Quiz averages over time
• Reduction in repeated errors (same misconception appearing week after week)
• Student attendance during remediation
• Teacher preparation time saved

If the pilot improves scores and consistency, scale slowly—school by school, department by department.

The quickest win is not “more talking.” It’s tighter practice cycles: attempt → feedback → retry. AI makes that cycle cheaper.

The WASSCE debate should be bigger than licensure

WASSCE performance is a system signal, not a single-person verdict. If the public conversation stays stuck on licensure, we’ll keep producing policies that feel tough but don’t change what happens between Monday and Friday in real classrooms.

For the AI ne Adwumafie ne Nwomasua Wɔ Ghana series, the stance is simple: use AI to strengthen teaching and learning routines, not to replace them. Let licensure do its job as a baseline requirement. Then invest energy into the daily supports that actually move results.

If you’re a school leader, a district official, or an education entrepreneur building solutions in Ghana, the next step is practical: start a small AI-supported assessment pilot, set clear integrity rules, and measure weekly learning gains. The story we should be arguing about next WASSCE season is not who to blame—it’s what worked.

What would change fastest in your school if every student received feedback within 24 hours, every week?