The pressure to adopt AI in education has never been higher. Vendors promise personalised learning, time savings, and closing achievement gaps. Most institutions say yes. Almost none have a repeatable process for deciding whether they should — or for knowing what to do when something goes wrong.
That gap between AI strategy and responsible action is exactly where institutions get hurt.
I research Explainable AI and trustworthy systems, and I've worked alongside education leaders navigating this decision in real time. The pattern is consistent: tools get deployed before the right questions get asked. Who audits the outputs? What happens to student data? What does the institution say to a parent, a board member, or a regulator when the AI makes a call no one can explain?
This session gives technology leaders a practical path from intent to implementation. You'll work through an AI evaluation framework built specifically for K–12 and higher education contexts — covering transparency, bias risk, and data accountability — and leave with a stakeholder communication strategy that brings faculty, administrators, and parents into AI decisions with clarity and confidence.
Strategy without a governance layer isn't leadership. It's risk. This session bridges the two.