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Last week our governance guru, Patricia Wanjama had the opportunity to train a Kenyan University Council on AI Governance. As we prepared for the training, the current AI picture in academia was fascinating:
❇️ Universities have professional certificates, executive education offerings, pre-university courses, Undergraduate, Masters and PhD pathways in Artificial Intelligence, Robotics and Intelligent Systems.
❇️ Research ecosystems are expanding around AI. Universities are publishing journals, convening conferences and collaborating on international research partnerships.
❇️ Institutional infrastructure is evolving. Centres of Excellence, AI & Coding hubs and digital partnerships are becoming common features. Some are deploying AI chatbots, developing AI teaching guides and integrating AI into student support systems.
❇️ Globally and locally, Chancellors and Vice Chancellors are publicly framing AI as central to the future of learning and work. This is shaping institutional posture.
The question for Councils, however, is not what programmes to launch — but how to govern AI responsibly and strategically. For them, their mandate is clear: They need to evolve from a “policing” culture to a “fluency” culture. During our session, we explored four pillars for a robust AI framework:
💡 Instead of just frowning on students use of AI, Lecturers should re-think assessment. Live case studies, oral presentations, framework development and requiring students “thinking rationale” annotations encourage critical thinking that a chatbot can’t mimic.
💡 The “Human-in-the-Loop” Philosophy: AI should boost economic efficiency, but never at the cost of data sovereignty. Is the University’s data ODPC compliant? Are they putting the “Student at the Center”? Also, use AI detection tools and monitoring systems sparingly.
💡 Technical Guardrails: Governance isn’t just about policy; it’s a risk appetite framework. Councils should consider dedicated AI committee discussions to chart the institution’s AI path.
💡 Continuous Monitoring:Consider automated dashboards to track policy infringements and quarterly macro-economic reports to spot new opportunities.
So as Kenyan Universities continue to clarify their institutional posture on AI, Councils should:
✔️ Define a formal AI risk appetite and develop a comprehensive AI policy.
✔️ Establish dedicated Council-level oversight, including co-opting technical expertise where necessary.
✔️ Conduct institution-wide AI audits to identify tools already in use.
✔️ Strengthen data protection and cybersecurity frameworks before scaling AI.
✔️ Introduce automated dashboards and require quarterly reporting on AI risks, opportunities and global developments.
AI in universities is now strategic. The institutions that will thrive are not those that adopt AI the fastest — but those that govern it best.
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