Kenya wants its STEM students to graduate knowing how to use AI: a warning that applies to the entire Global South

By Business Daily Africa (Kenya) · July 8, 2026.

Victor M. Mwongera, national director of Young Scientists Kenya, signs a column in Business Daily Africa with a simple but uncomfortable argument: Kenya has set the goal of having 60% of upper secondary students pursue the STEM track within its new Competency Based Education model, but that objective risks remaining on paper if students do not learn to work with AI before graduating. His thesis is that AI tools are becoming a workplace skill as basic as handling a spreadsheet, and that employers no longer value technical knowledge alone, but rather the ability to combine it with AI to analyze data, speed up decisions and solve problems. The article itself openly acknowledges the obstacles: limited school infrastructure, inadequately trained teachers and insufficient funding to sustain the education reform underway.

The approach connects with something we have already seen repeated in the education sector globally: the winning school is neither the one that bans AI nor the one that uses it without judgment, but the one that trains teachers capable of orchestrating it as a tool for augmented teaching. Mwongera senses this when he calls for teachers to receive training in data privacy, critical thinking and risk management, not just basic digital literacy. It is the same distinction that separates, in any profession, those who hand their judgment over to the machine from those who use it to expand their own.

What is relevant about this column is not its technical content —it is an opinion piece, with no hard data beyond the official 60% target— but the place from which it is written. The conversation about AI and employment tends to center on Silicon Valley, Beijing or Brussels; here it is framed from Nairobi, in a country competing to position its young workforce in sectors such as agriculture, health, energy and manufacturing. If generative AI delivers on its promise of cheapening capabilities that once required years of specialized training, the countries that today start with less technological infrastructure have, paradoxically, more to gain from a well-executed training leap: they can skip intermediate stages of traditional economic development, just as happened with mobile telephony over fixed-line networks. That is, at its core, the argument of abundance applied to education: AI as a lever that levels opportunities between economies with very different starting points, not merely as a threat of replacement.

But it is worth not losing sight of the short-term nuance the author himself admits. An ambitious curricular goal without a budget, without stable connectivity and without trained teachers does not automatically translate into prepared students; it translates into one more gap between the schools that can afford AI tools and those that cannot. The hard transition we usually talk about in the labor arena has its educational version here: whoever is late in equipping their students with these skills will lose not only future jobs, but the possibility of taking part in the next wave of economic growth. The 60% STEM target is a sound statement of intent; what will determine whether Kenya —or any other country in the same position— manages to capitalize on it is whether it can close that execution gap before today's students reach tomorrow's labor market.