AI Training for Schools and Universities in Kenya: What It Should Include

TL;DR: - Most institutions are past deciding whether students use AI. The real choice left is whether there's a clear policy. - Train lecturers and administrative staff before rolling out rules to students; inconsistent enforcement is what actually breaks a policy. - A workable policy names which AI uses are encouraged, which need disclosure, and which aren't allowed, in plain language. - Administrative and registry staff get real, low-risk productivity gains from AI too, this shouldn't get lost in the integrity conversation.

Start with lecturers and teachers, not students

An institution that rolls out an AI policy for students without first training its own staff is building on sand. Lecturers and teachers need to understand what AI-assisted work actually looks like, how to design assessments that are harder to complete with AI alone, and how to have a fair, evidence-based conversation with a student when AI misuse is suspected, rather than a guess based on writing style. That last point matters more than institutions often expect: accusations based on "this doesn't sound like the student's usual writing" without real evidence create disputes nobody wins.

What administrative and support staff need separately

Registry, admissions, and administrative staff have a different, more immediate use case: AI genuinely speeds up drafting correspondence, summarising long policy documents, and handling repetitive written tasks. This group often gets left out of AI training because the conversation defaults to "AI and academic integrity," but the productivity gains here are real and low-risk.

Building the actual policy

A workable AI policy for a school or university needs to answer a small number of concrete questions: which AI uses are encouraged (brainstorming, understanding a concept, checking grammar), which require disclosure (AI-assisted drafting of an assignment), and which are not allowed (AI producing the substantive content of graded work without disclosure). Vague policies that say "use AI responsibly" without defining what that means in practice tend to produce inconsistent enforcement and student confusion.

Why staff training has to come before student-facing rules

An institution that publishes an AI policy for students before its own staff understand AI well enough to apply it consistently will find the policy falls apart in practice, different departments interpreting it differently, different lecturers enforcing it differently. Training staff first, then rolling out clear rules to students, is the order that actually holds.

Where FuKazee fits in

FuKazee has delivered AI training for education audiences, including postgraduate researchers through ScholarShift Accelerator, and works with institutions building staff-first AI training and policy support. If your school or university is thinking through how to approach this, email lead@fukazee.com to talk through what a staff training session would need to cover.