Online Reporter
Prof Willie Chinyamurindi
Zimbabwe’s recently launched National Artificial Intelligence (AI) Strategy is ambitious.
IT speaks of transforming the country into a regional hub for AI innovation, developing AI talent, modernising agriculture and mining, improving healthcare and building a digitally empowered society.
It is a vision rooted in the belief that Zimbabwe should not merely consume technology developed elsewhere but become a creator of its own solutions.
The strategy is explicit in its aspiration to develop local talent, strengthen AI literacy and ensure the country participates meaningfully in shaping the future.
Yet there is an uncomfortable question we need to ask.
Are Zimbabwean universities preparing students for this future, or are we still preparing them for a world that no longer exists? Across the country, the conversation about AI in higher education has become dominated by fear.
Students are accused of using AI.
Lecturers are encouraged to detect AI. Universities are investing in policies, guidelines and surveillance mechanisms.
The underlying assumption appears simple: AI is a threat that must be controlled. But what if we are focusing on the wrong problem?
The reality is that AI is already here. Students are using it. Employers are using it. Government departments are beginning to explore it. The banking sector is exploring it. The health sector will increasingly depend on it.
Zimbabwe’s own AI strategy identifies AI as a central driver of national development.
Yet, at many universities, students continue to complete assignments designed for a pre-AI world.
We ask students to define concepts.
We ask them to summarise theories.
We ask them to reproduce information.
Then we act surprised when AI can do the same thing in seconds.
The problem may not be the student.
The problem may be the assignment.
If an assessment can be completed effectively by a machine, perhaps the question is not whether students are cheating.
Perhaps the question is whether lecturers are still assessing the right things.
This matters because Zimbabwe’s AI strategy places extraordinary emphasis on talent development and AI literacy.
It envisions an education system that equips learners with the capabilities needed to thrive in an AI-driven economy.
Yet genuine AI literacy is not simply learning how to use ChatGPT or other tools.
It is learning how to question AI, critique AI, verify AI and apply judgement when using AI. Ironically, many universities are doing the opposite.
Rather than teaching students how to use AI responsibly, we are often attempting to create environments where use of this technology is hidden.
Students learn quickly that honesty about AI usage may attract suspicion.
The result is not responsible AI use. It is secret AI use.
That outcome serves nobody.
Zimbabwe’s AI strategy repeatedly speaks about innovation, entrepreneurship and developing home-grown solutions.
But innovation does not emerge from an environments characterised by fear. It emerges from environments marked by experimentation, critical inquiry and responsible risk-taking.
The challenge is even greater when we consider employability.
Imagine graduating a student who has never learned how to critically engage with AI tools.
Imagine sending that graduate into a banking sector that uses AI.
Into an agriculture sector using predictive analytics.
Into a healthcare system increasingly supported by intelligent systems.
Into a mining industry exploring AI-driven optimisation.
Who exactly are we preparing students for?
Certainly not the Zimbabwe envisioned in the National AI Strategy.
Perhaps the most uncomfortable question is directed at lecturers themselves.
How many of us have received meaningful AI training?
How many universities have systematically developed AI capability among academic staff?
How many lecturers feel confident discussing algorithmic bias, hallucinations, data governance, AI ethics or prompt engineering? It is difficult to prepare students for a future we ourselves are still trying to understand.
The Zimbabwe National AI Strategy correctly identifies talent development as a national priority. Yet talent development cannot begin and end with students.
Universities need a national programme of AI capability development for lecturers.
Before we can teach AI literacy, we must become AI literate ourselves.
Most importantly, universities need to move from surveillance to stewardship.
The future will not belong to universities that become experts at catching students using AI. It will belong to universities that become experts at teaching students how to use AI ethically, critically and creatively.
The question before Zimbabwean higher education is, therefore, not technological. It is educational.
We have a national strategy that imagines Zimbabwe becoming a leader in AI for development.
The real test is whether our universities are willing to transform themselves sufficiently to make that vision possible.
If we fail to do so, we may discover a painful irony. Zimbabwe could have an AI strategy for the future while its universities continue teaching for the past.
Prof Willie Chinyamurindi is based at the University of Fort Hare in South Africa, within the Department of Applied Management Administration and Ethical Leadership. He is also a PhD supervisor for two Zimbabwean universities.
I challenge anyone who knows what AI is, how it must be used and what it must be used for, how AI tools are created, and by who, from what and using what to come forward and explain. For most laymen anything done automatically by machines has become AI driven. Recently we heard the country going abuzz calling simple engineering instrumentation coupled to measurement, telemetry and computer control systems on grain silos as AI driven systems. A non technical person can be forgiven to believe everything automatically done by a machine is AI driven but the truth is very few systems, if any, operate in the AI domain in this country. In agriculture (grain silos control, weather prediction, crop monitoring etc) are not new AI driven systems. They have been in existence for years. In offices and social interactions, the use of bots and other biodata related functions is the closest we are to using AI driven systems. But do we know what this is all about? And at very lower levels, research and report writing are now beginning to use AI. Again the question is, do people know what goes on behind the scenes when using these AI tools? What we need as country is to first understand fully what AI is all about. For now it appears like we are trying to use a gun to shoot when we don’t know the basics of operating it. And that is pretty dangerous.