Using AI To Create Equitable Funding Formulas
Using AI to vet and develop funding formulas taking into account equity factors
Overview
We have used the same formula for many years to allocated LAP funds to the elementary schools. As costs rose and budgets remained the same we needed to reevaluate the supports driven out to the schools and use an equitable, fair method to determine which schools were most in need.
Prompt Used
Create a weighted average model that takes into account building size (enrollment), Free and Reduced Lunch (FRL) rates, and the number of students needing reading intervention. Define the weighting criteria for each factor. Given the budget of $1,100,000 and an average teacher salary of $150,000 for 1.0 FTE, develop a rank ordered list of schools based on need and what the allocation of fte should be.
Other Content Provided
Building enrollment, free and reduced lunch rates and the number or students not at benchmark in grades K-3.
Any Other Info
I used this prompt and was able to change the weighted average of each area to see how it would impact the schools. For example, I used 20% size, 40% FRL and 40% reading scores and then adjusted the weighting model each time I ran it to see if there were patterns or schools that emerged as most in need over and over.