This Article examines whether incorporating data mining technologies in education can promote equality. Following many other spheres in life, big data technologies that include creating, collecting and analyzing vast amounts of data about individuals, are increasingly being used in schools. This process has already elicited much interest among scholars, parents, and the public at large. Yet this attention has largely focused on aspects of student privacy and data protection, and overlooked the profound effects data mining may have on educational equality. The Article embarks on this task by focusing on one educational practice, namely ability grouping, that is already being transformed by educational data mining. Ability grouping is the practice of separating students into classes or tracks according to their perceived academic abilities. While some educators support the practice, arguing that it allows teachers to adjust themselves to the needs of their students, critics argue that ability grouping reinforces educational inequalities. Implicit biases that pervade educational decision-making processes result in the stratification of students from racial and ethnic minorities and students from poor families to lower tracks in which they receive inferior education and limited opportunities. Given the well documented biases in traditional ability grouping, Data Driven Ability Grouping – the use of algorithms to inform assignment decisions – may be a step in the right direction. However, as the Article demonstrates, the use of data mining technologies for ability grouping creates a whole host of unique challenges in terms of educational equality. The Article argues that traditional doctrines of equal protection will be unable to contend with the biases that data driven ability groping is likely to create. Instead, the Article offers a novel approach to the legal regulation of data driven ability grouping that involves integrating legal and technological expertise and creating equality-sensitive algorithms. The combination between legal and technological solutions can ensure that data driven ability grouping decreases biases in ability grouping and promotes educational equality.