Researchers at the American Enterprise Institute developed an Absence Forecast tool aimed at predicting which students are likely to become chronically absent before the school year fully starts. The approach uses district- and state-level attendance data districts already possess—such as past-year monthly patterns, early-year absences, grade level, and free-and-reduced-price lunch eligibility—to generate a risk score from 0 to 100%. The tool is positioned as an operational early-warning system that does not require researchers to run, with the intent of helping districts intervene earlier using targeted communication and supports. AEI reports that chronic absenteeism peaked around 28% in the 2021–22 school year, though the tool’s developers say rates have not returned to pre-pandemic levels. For higher education leaders tied to teacher preparation, partnerships with K-12 districts, and student pipeline initiatives, the development is a signal of where data-driven student success interventions are heading: earlier risk detection, tighter use of existing data, and scalable forecasting across systems.