Nevada’s Pupil-Centered Funding Plan (PCFP) is the first in the nation to use a machine learning-based at-risk indicator (the GRAD score) to allocate funding based on students’ likelihood of not graduating. The indicator closely aligns with traditional measures of need—such as identifying students with IEPs, English Learners, and foster youth—but provides a more precise, targeted approach.

Because GRAD scores change yearly, the specific students receiving at-risk funding can shift, complicating school- and district-level budget planning. While interventions in these schools have improved math and English outcomes overall, gains for students identified as at risk have been more modest, highlighting the need for continued evaluation of how targeted funding translates into improved student achievement.