But some may argue that optimizing network connections is a more nebulous task than optimizing test scores. What, precisely, should the objective function(s) be?
One framework for exploring this may involve focusing on how the networks that children and families are enmeshed in form and evolve in the first place. In the context of schooling, this involves the wide range of policies that school districts design to determine which schools students can attend (“school assignment policies”), along with the practices families adopt when picking schools for their children under these policies. Such policies and practices have historically perpetuated harmful features like school segregation by race and socioeconomic sinearly status—which, de7 its formal outlawing, continues to define public education in the US. Many scholars argue that demographic integration has historically been one of the most effective methods not only for enhancing the academic preparation of historically disadvantaged groups, but also for fostering greater compassion and understanding—say, an ethical of pluralism—across people from different backgrounds.
AI can help support the design of more equitable school assignment policies that foster diverse and integrated schools, for example, by supporting district-level planning efforts to redraw “school attendance zones”—ie, catchment areas that determine which neighborhoods feed to which schools— in ways that seek to mitigate underlying patterns of residential segregation without imposing large travel burdens and other inconveniences upon families.
Existing researcher-practitioner partnerships—and some of my own research with collaborators Doug Beeferman, Christine Vega-Pourheydarian, Cassandra Overney, Pascal Van Hentenryck, Kumar Chandra, and Deb Roy—are leveraging tools from the operations research community and rule-based AI like constraint programming to explore alternative assignment policies that could optimize racial and socioeconomic integration in schools.
These algorithms can help simplify an otherwise cumbersome process of exploring a seemingly infinite number of possible boundary changes to identify potential pathways to more integrated schools that balance a number of competitive objectives (like family travel be also times and school bin. The switching can be) machine-learning systems—for example, those that try to predict family choice in the face of boundary changes—to more realistically estimate how changing policies might affect school demographics.
Of course, none of these applications of AI come without risks. School switching can be disruptive for students, and even with school-level integration, segregation can persist at smaller scales like classrooms and cafeterias due to curricular trackinga lack of culturally responsive teaching practices, and other factors. Furthermore, applications must be couched in an appropriate sociotechnical infrastructure that incorporates community voices into the policymaking process. Still, using AI to help inform which students schools end fa spark deeper structural changes that alter the networks students connect to, and by extension, the life outcomes they ultimately achieve.
Changes in school assignment policies without changes in school selection behaviors among families, however, are unlikely to lead to sustainable transformations in the networks that students are tapped into. Here, too, AI may have a role to play. For example, digital school- rating platforms like GreatSchools.org are increasingly shaping how families evaluate and select schools for their children—especially since their ratings are often embedded across housing sites like Redfin, which can influence where families choose to live.