Student information includes information collected about students by schools, states and third-party providers. Learn what it includes, when information becomes personally identifiable and why recognizing what can identify a student matters.

Educational products promise personalized learning, actionable data and improved achievement. Those claims may be true, but leaders still need evidence that matches the claim, demonstrates learning beyond the product and reflects implementation conditions they can realistically reproduce.

Assessment and testing are often treated as interchangeable, but they aren’t the same thing. A test is one way to gather evidence. Assessment is the larger process of determining what we need to understand, gathering and interpreting evidence and deciding what happens next.

AI can make educational work faster and easier, but sometimes doing the work is how people become capable. As AI enters classrooms, educators must distinguish between work technology should automate and the productive struggle students and teachers still need.

States and districts are beginning to scrutinize student screen time during the school day. Before limits dictate practice, educational leaders should examine which digital experiences genuinely serve learning and which exist simply because technology became the default.

Year-over-year test scores can reveal meaningful patterns, but small changes do not automatically explain what caused them. Responsible interpretation requires context, implementation evidence and the patience to distinguish sustained improvement from ordinary variation.

“Snake-breeding behavior” occurs when people optimize measure outcomes while undermining the purpose the measure was created to serve. Through the lens of Compassionate Assessment, Dr. Mary Cochron explains how this pattern emerges and why leaders must continually return to purpose.

Artificial intelligence can generate information in seconds, but it cannot replace human judgment. Education’s responsibility is not simply to teach students how to use AI, but how to think critically, evaluate responsibly, and exercise discernment in an AI-rich world.

Data meetings often begin with scores and end with conclusions. Meaningful interpretation requires something more: implementation evidence, context, and the discipline to stay curious longer before rushing to judgment.