2A: Removing Barriers to Learn From Variation: AI Control Chart Generator

Join us to engage in the process of learning from variation. We’ll use public data and real examples to show how control charts surface “special cause variation” supporting learning across schools, districts, and improvement communities. We’ll also confront a common barrier: improvers often need to learn R, Python, or Excel add-ins just to make a control chart. In this session, we’ll demo a purpose-built GPT that generates accurate control charts from your uploaded data —no coding required—so networks can quickly see patterns, spot special causes, and go learn with those achieving meaningfully different results. We’ll walk through the RAISE network bright-spot method as a concrete case, then engage in the process using a lightweight “learning from variation” protocol you can apply to your data.