By Mary Oge Chuks
A service can be available in a country without being comfortable to use in the language of everyday life.
That distinction deserves a larger place in discussions about African AI adoption. An interface may offer polished English while struggling with the expressions, mixed-language sentences or local context through which someone explains a real problem.
The World Bank’s World Development Report 2026 argues that adopting existing AI is insufficient on its own: tools also need adaptation to local languages, institutions, information and development needs. It highlights accessible delivery, including basic-phone and voice-based approaches. Source: World Bank
Translation is a starting point
My practical interpretation is that a language test should extend beyond translating a prepared paragraph.
Ask whether a person can explain a task naturally, receive an understandable answer and correct a misunderstanding. Include ordinary vocabulary and the kinds of phrasing people actually use. Where speakers mix languages, include that too.
A fluent-looking answer is not enough. Local speakers need to assess whether the meaning survived and whether the response fits the situation.
Who gets to define a good answer?
A useful evaluation team should include people who use the service, alongside language specialists and technical staff. A grammatically correct explanation can still be confusing, inappropriate or too formal for its audience.
Consider a hypothetical agricultural information service. It would need to handle local crop names and communicate uncertainty clearly. It should also distinguish general information from advice requiring a qualified local professional. This is an illustrative design case, not a claim about a specific deployed service.
The questions change with the setting. A classroom tool, a business assistant and a public-information service will not have identical standards.
Build a route for correction
When the system misunderstands a phrase, users need a way to report the problem without writing a technical explanation. Developers should be able to examine patterns of failure while respecting the people whose information is involved.
Participation also deserves recognition. Language work is skilled work; it should not disappear behind a generic claim that the system supports a community.
Africa’s AI opportunity includes building and testing services that fit people’s lives. Local language is one part of that work, alongside affordability, infrastructure and capable institutions.
A strong question for any demonstration is: can the intended user explain an ordinary problem in their own words and recognise a useful answer?
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