But some might argue that optimizing network connectivity is more of a fantasy than optimizing checkpoints. What exactly should the objective function(s) be?
A framework for exploring this might involve focusing on how the networks in which children and families are bound form and develop in the first place. In the context of school attendance, this involves a variety of policies that school districts design to determine which schools students may attend (“district policies”), along with the practices that families apply when choosing a school for your child in accordance with these policies. Such policies and practices have historically perpetuated harmful features such as school segregation by race and socioeconomic status, although it has been nearly 70 years since it was officially excluded. round of law, still going on define general education United States. Many scholars consider that demographic integration has historically been one of the The most effective method not only to enhance the academic preparation of historically disadvantaged groups, but also for cultivate greater compassion and understanding—for example, a morality of Pluralism—for people from different backgrounds.
AI can help support the design of more equitable school allocation policies that promote diverse and inclusive schools, for example by supporting district-level planning efforts to redraw “school zones”—that is, watersheds that determine which neighborhoods provide which schools—in ways that seek to reduce underlying patterns of population segregation without burdening heavy travel and other inconveniences for families.
now available researcher-practice partnership—and some of my own research with collaborators Doug Beeferman, Christine Vega-Pourheydarian, Cassandra Overney, Pascal Van Hentenryck, Kumar Chandra and Deb Roy—which is leveraging tools from the operations research and rule-based AI community like binding programming to explore alternative assignment policies that can optimize socioeconomic and racial integration in schools.
These algorithms can help simplify another complex process of exploring the multitude of possible boundary changes to identify potential pathways to more integrated schools that balance some competitive goals (such as family travel time and school transitions). They can also be combined with machine learning systems—for example, those that try to predict family choices in the face of boundary changes—for a more realistic estimate of political change. How books can affect school demographics.
Of course, no application of AI is without risk. Transferring schools can be difficult for students, and even with integration at the school level, segregation can exist on smaller scales such as classrooms and cafeterias. extracurricular tracking, lack of culturally responsive teaching methods and other factors. Furthermore, applications must be housed in an appropriate socio-technical infrastructure, incorporating the voice of the community into the policy-making process. However, using AI to help inform which students and families attend the same school could create more profound structural changes that change the networks students connect to and, more broadly, life outcomes they ultimately achieve.
However, changes in school allocation policy without changes in school choice behavior among families are unlikely to lead to sustained transformations in the networks in which students participate. Here too, AI can play a role. For example, digital school assessment platforms like GreatSchools.org are increasingly shaping the way families evaluate and choose schools for their children—especially since their ratings are often embedded on housing sites like Redfin, which can affect where families choose to live.
