by Carlene Kyeremeh
It was late, and I was building slides on fiscal policy for a class of twenty, most of whom had never taken economics before. I asked an AI tool for a worked example of expansionary policy — something concrete enough to anchor the mechanism. It answered in seconds: a government cuts taxes and raises infrastructure spending; roads get built; contractors hire; demand rises. Clean. Usable. American. The mechanism travelled, but the roads were interstate highways, and the government spending the money was not one my students would ever petition. The example had arrived so quickly, and so confidently, that I nearly pasted it in. For a moment, convenience almost became curriculum.
Then, in the same session, the same tool did something I could not have done as well alone. I asked it to scaffold the vocabulary — a glossary that assumed nothing and met students who did not yet have the words. It was patient in a way I am not always patient at that hour. It found analogies; it sequenced the terms so that each rested on the one before. That slide was better for the machine’s help.
Within a single sitting, the tool had lowered the barrier for my students and quietly imported the exact default my work exists to help people notice. Across three MBA courses this term, I began to see the same pattern: the tool improved many aspects of how I prepared to teach, and became least dependable where the teaching required local knowledge, current evidence or an institution named correctly. The help landed on form. The failures landed on specificity.
In my international trade finance course, I extend a running case — Adom Naturals Ltd, a Kumasi agribusiness I built so that trade finance stops being abstract and acquires a location, a product and a currency exposure. I asked the tool to situate the firm against current conditions: what the African Continental Free Trade Area (AfCFTA) changes for a business like this, and where COCOBOD now sits in the picture. It answered in assured paragraphs and told the familiar story: Ghana grows some of the world’s finest cocoa, ships much of it out with limited processing, and watches the greater share of value accrue downstream. Fluent, orderly, and a season out of date.
The confident narrator
It missed what I happened to be holding in a government source that week: in February 2026, Cabinet directed that, from the 2026/27 crop season, a minimum of 50 per cent of Ghana’s cocoa beans should be processed locally. For Adom Naturals, that reform changes the opportunity set. Cocoa liquor, butter, cake and other processed products can retain more value within Ghana and may qualify for preferential treatment in African markets where the relevant AfCFTA rules of origin and tariff requirements are satisfied. The tool had narrated the extractive arrangement in the present tense and missed the policy intended to change it.
Nothing marked the claim as stale. The tool warns that it can make mistakes, in a line printed beneath every answer, but a caveat attached equally to everything is not calibration; it is the absence of it, dressed as candour. A colleague says: I am sure of this; check me on that. The tool says it might be wrong about anything, then says everything in the same even voice. The danger was never that it made mistakes. Every source makes mistakes. The danger was that it sounded exactly as certain when it was wrong as when it was right.
The wrong institution
Financial regulation showed me the problem from another angle. Ask a general-purpose tool about capital adequacy, disclosure or market conduct and it is fluent, because the published record is thick with Basel standards, US and UK regimes, and decades of commentary. Ask it to route the same questions through Ghana’s regulatory architecture and the fluency thins.
When I asked which body supervises an insurer in Ghana, and then which oversees a securities offering, it reached both times, confidently, for the Bank of Ghana. The answer was plausible because the central bank is prominent in Ghana’s financial system. It was nevertheless wrong. Insurance supervision belongs to the National Insurance Commission under the Insurance Act, 2021; securities-market regulation belongs to the Securities and Exchange Commission under the Securities Industry Act, 2016, as amended. When I named the specific commissions, the tool corrected itself at once. The information was retrievable; it was not the default.
Three defaults, one voice
Set the three moments side by side and a more complicated pattern emerges. The fiscal-policy example exposed a geographical default; the cocoa case, a temporal one; and the regulation case, an institutional one. These were different failures, but they arrived in the same confident voice.
I cannot inspect the tool’s training archive, so I cannot attribute every error to missing data alone. A stale policy claim may reflect a knowledge cut-off or the absence of live search. A regulatory error may reflect weak retrieval, poor weighting or the greater prominence of a general institution over a specialised one. What I can observe is an asymmetry of retrieval: general and North Atlantic formulations arrived unprompted, while Ghanaian specificity had to be named, sourced and verified into view.
That asymmetry belongs in the larger conversation about AI and epistemic justice — about whose knowledge is dense enough, accessible enough and prominent enough to be retrieved fluently, and whose is thin enough to be flattened, displaced or missed. The tool did not invent the hierarchy of whose knowledge counts. It inherited a record shaped by that hierarchy and can reproduce it at scale, in fluent prose. Better models may reduce some errors, but model improvement alone cannot repair knowledge that remains absent, inaccessible or systematically underrepresented.
I develop that argument more formally elsewhere, in work currently under review. Here, I want only to report what it looks like from inside three classrooms, at the point where defaults become examples and examples become curriculum.
Verification is the work
I use these tools daily and they earn their place, so let me be honest about the difficulty. The answer is not refusal; refusing the help is not a decolonial act, only less help. The answer is the discipline I have argued for all along, now turned on the machine: no sentence enters the curriculum until it points to a source I can hold. Verification is not the friction that slows the tool down. With a tool like this, verification is the work.
I have also begun turning that work into a learning activity. I place selected AI outputs beside the relevant primary or institutional sources and ask students to identify what the model has generalised, dated or assigned to the wrong body. Verification becomes not only my quality-control procedure but part of the curriculum itself.
Perhaps that is the graduate skill this moment now asks for. Not simply how to find information, the tool is generous with information, but how to test information whose presentation gives no sign whether it has earned our trust. The scarce skill is no longer retrieval. It is discernment. Teaching has always required two kinds of expertise: explaining ideas well, and knowing where they belong. The tool is becoming remarkably good at the first; the second is still ours. It narrates beautifully — but a narrator is not a witness, and decolonising the curriculum now includes learning to interrogate the archive that speaks back.
If your tool has ever been confidently wrong about your own institution, your own regulator or your own country’s data, I would like to know what it got wrong — and whether a student would have caught it.
Dr Carlene Kyeremeh is an Associate Professor and Vice President, University Advancement, Recruitment & Research, at All Nations University, Ghana, where she teaches managerial economics and international trade and finance on the MBA programme. Her research examines decolonial curriculum reform, gender equity and academic mobility in African higher education, with the African Continental Free Trade Area as a recurring empirical anchor. She is currently researching the reintegration of diaspora-return faculty in Ghanaian universities. She writes The Decolonized Curriculum, a newsletter on curriculum decolonisation in African higher education.
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Author’s note: This article is adapted and substantially expanded from Issue 13 of The Decolonized Curriculum.
