Data Analytics in the Age of AI by Leveraging Knowledge Management

Generative AI has quietly redefined what data-driven means in K-12. It is no longer just dashboards and test scores, but algorithmic systems making inferences about students that deserve scrutiny. Led by a district CIO with a background in applied mathematics and research on AI bias detection, this session explores what genuine data-driven decision-making looks like when AI models sit between raw data and district decisions. You will learn a framework for auditing AI-generated insights before acting on them, how to detect algorithmic bias in analytics tools before it affects student outcomes, and how to build data literacy among staff who increasingly consume AI-summarized data rather than raw numbers. Drawing on research in bias detection and causal inference alongside real district analytics experience, the session shows how AI can help make sense of large volumes of data from many sources while keeping resource decisions anchored in evidence. You will leave with a practical checklist for judging whether an AI-powered analytics tool is truly trustworthy.